Graduate Seminars
Spring 2024
May 3, 2024
Swarming dynamics: From Nature to Theory to Robotics
Ira B. Schwartz, US Naval Research Laboratory
When: Friday, May 3, 2024, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Swarming behavior continues to be a subject of immense interest because of its centrality in many naturally occurring systems in physics and biology, as well as its importance in engineering applications such as robotics. Here we examine the effects on coherent swarm pattern formation from aspects of communication, such as latency effects, link topology and environmental uncertainty. With the availability of ever more cheap and powerful computing, interest in the use of mixed-reality and swarm experiments has grown considerably in the physical sciences.
In this talk, we consider the pattern formation of delay - coupled swarms theoretically and experimentally. Motivated by physical experiments, we then consider a model of a mixed-reality system, and show how noise in the physical part of the system can influence the virtual dynamics through a large fluctuation, even when there is no noise in the virtual components. We quantify the effects of uncertainty by showing how characteristic times of noise induced switching between swarm patterns scale as a function of the coupling between the real and virtual parts of the experiment. Finally, although the dynamics and pattern formation of single delay coupled swarms is fairly well known, colliding swarms and their resulting dynamics is lacking solid theoretical foundations. Here we consider single and colliding swarms of various sizes operating with communication networks having delay. We show numerically that the resulting interacting swarms may scatter, flock as one, or enter a stable rotational state in which one swarm captures another. Analytically, we show a mean-field approach can be used to predict parameters under which colliding swarms form a rotational milling state.
This work is done in collaboration with Klimka Szwaykowska, Sayomi Kamimoto, Thomas Carr, Victoria Edwards and Jason Hindes.
Bio
Ira Schwartz received his Ph.D. in applied mathematics from the University of Maryland in 1980 and then was awarded a National Institutes of Health fellowship to work in mathematical biology, during which he studied chaotic systems in population dynamics, with an emphasis on epidemiology. At his current position at the Naval Research Laboratory, he heads the Nonlinear Systems Dynamics Section. The main themes of his work have been mathematical and numerical techniques of nonlinear dynamics and chaos, and most recently, nonlinear stochastic analysis and control of coupled systems and networks. He has applied these tools to the study of numerous multi-agent systems modeled by continuous and discrete systems such as swarms, nonlinear optics, power grids and epidemics. Many of his theories have transitioned to inventions, and patents. Dr. Schwartz is an APS fellow,and was awarded the Navy Tech Transfer Award, and the Sigma Xi pure science and Alan Berman publication awards. He was elected and served as vice-chair to the SIAM Applied Dynamics society (2017-2019), which he helped co-found.
April 26, 2024
Emergence of Synchronization Patterns in Neuronal Networks: A Focus on Central Pattern Generators in Insect locomotion
Zahra Aminzare, University of Iowa
When: Friday, April 26, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Synchronization refers broadly to patterns of coordinated behaviors that emerge spontaneously or by design in natural and artificial complex network systems and are crucial for reliable functioning in such networks. Achieving stable and robust synchronization relies on the dynamics of individuals, properties of their connections, exogenous control inputs, and the network’s topology. In this seminar, we'll explore Central Pattern Generators (CPGs), neural networks responsible for coordinating activities like walking. We will begin by introducing a mathematical model for CPGs and use this framework to investigate the emergence of various stable and robust synchronization patterns. These patterns correspond to distinct locomotion gait patterns, and we will discuss how they transition from one to another. Additionally, our discussion will encompass the prediction and mitigation of pathological gait patterns within the model. We will showcase how heterogeneity can eliminate these abnormal locomotion patterns, shedding light on potential therapeutic applications.
Bio
Dr. Zahra Aminzare is an Assistant Professor in the Department of Mathematics at the University of Iowa, with affiliations in the Applied Mathematical and Computational Sciences Program and The Interdisciplinary Graduate Program in Neuroscience. She is also a member of The Iowa Neuroscience Institute. Dr. Aminzare’s research interests are centered around the intersection of applied dynamical systems, partial differential equations, and mathematical biology. Specifically, her work focuses on contraction theory and synchronization patterns in biological systems. Before joining the University of Iowa, Dr. Aminzare served as a Postdoctoral Research Associate at PACM, Princeton University, from 2015 to 2018. She completed her Ph.D. in Mathematics at Rutgers University in 2015.
April 19, 2024
Data Mining and Machine Learning for Analysis of Network Traffic
Ljiljana Trajkovic, Simon Fraser University
When: Friday, April 19, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Collection and analysis of data from deployed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while various machine learning techniques proved valuable for predicting anomalous traffic behavior. In described case studies, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies.
Bio
Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She served as a Distinguished Lecturer of the IEEE Circuits and System Society and a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. She is a Fellow of the IEEE.
April 12, 2024
Stability of synchronized Kuramoto networks
Melvyn Sandy Tyloo, Los Alamos National Laboratory (LANL)
When: Friday, April 12, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Phase oscillators are widely used to model natural and technological systems. Depending on the internal parameters of the interacting dynamical systems and the strength of their coupling, interesting collective behavior might emerge.
A paradigmatic model to describe the synchronization dynamics of coupled phase oscillators is the one of Kuramoto, which was initially derived to described weakly coupled biological oscillators but turned out to be useful for a broader rang of dynamical systems.
In this presentation, I will focus on the stability of networks of Kuramoto oscillators subject to external perturbations. In particular, I will highlight the interplay between network structure, internal parameters and characteristics of the perturbations in the resilience of synchronized oscillators.
Bio
Melvyn obtained his master degree and PhD in Theoretical Physics at the Swiss Federal Institute of Technology in Lausanne (EPFL) respectively in 2016 and 2020. He is currently a Director's Postdoc Fellow at the Los Alamos National Laboratory (LANL) and also affiliated with the Center for Nonlinear Studies (CNLS). His research focuses on complex network-coupled dynamical systems and the identification of their local/global vulnerabilities against external perturbations.
April 5, 2024
Analyzing Dynamics of Networked Systems Using Graph Signal Processing: Case Studies on Smart Grids
Mia Naeini, University of South Florida (USF)
When: Friday, April 5, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Many natural and engineered systems consist of interconnected components that their dynamics and interactions collectively define the state and behavior of the system. Examples of such systems include cyber physical systems, such as smart grids and smart critical infrastructures. With advancements in sensing, monitoring technologies, and the availability of vast amounts of data, coupled with the adoption of data analytics and machine learning techniques, new opportunities have emerged to analyze, understand, and enhance the operation and behavior of these systems. To analyze data from networked systems, the fast-growing field of Graph Signal Processing (GSP) offers a fresh perspective and technical paradigm. GSP extends the classical signal processing techniques and tools to irregular graph domain, which makes it suitable for analyzing structured data and the dynamics of systems with interconnected components. In this talk, we will delve into key concepts of GSP and explore their applications in analyzing data from networked systems, using smart grids as a running example. Through the lens of GSP, new studies will be presented that uncover new insights into the dynamics and behavior of smart grids under various stresses. Specifically, the focus will be on the spreadability of single node perturbation in the system, as well as state information analytics for state recovery and stress detection. These discussions will shed light on the transformative capabilities of GSP and its potential to enhance the understanding and operation of networked systems.
Bio
Dr. Mia Naeini is an Assistant Professor in the Department of Electrical Engineering, University of South Florida (USF). Before joining USF, she was an Assistant Professor in the Computer Science Department at Texas Tech University (TTU). She received her Ph.D. degree in Electrical and Computer Engineering with a minor in Mathematics from University of New Mexico (UNM) in 2014. Her research interests include leveraging data analytics, network science, signal processing, graph signal processing and graph-empowered machine learning to integrate security and reliability measures as well as socio-behavioral models into the design and control of cyber physical- human (CPH) systems with a focus on smart grids. Her research has been supported by various funding agencies, including National Science Foundation (NSF), Defense Threat Reduction Agency, and Florida Center for Cybersecurity. She has received TTU New Faculty Award in 2017 for excellence in teaching and research, one of the Best Conference Papers at the IEEE PES General Meeting in 2022, and the NSF CAREER award in 2023. She has served as the associate editor of the IEEE Communication Letters and as the chair and technical program committee member of several workshops and conferences in the area of power and communication systems. She is a senior member of IEEE.
March 29, 2024
Modeling Coordinate Transformations in Neural and Neuromorphic Systems
Frances Chance, Sandia National Laboratories
When: Friday, March 29, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Animals excel at a wide range behaviors, many of which require that the animal’s neural circuits perform fast and efficient calculations. My recent research has focused on applying current understanding of biological nervous systems to the development of novel neuromorphic (brain-based) architectures. I will present a neural network model, inspired by the dragonfly nervous system, that calculates turning for successful prey interception. The model relies upon a coordinate transformation from eye-coordinates to body-coordinates, an operation that must be performed by almost any animal nervous system relying upon sensory information to interact with the external world. I will also describe a neuromorphic implementation of the dragonfly model that leverages dendritic shunting inhibition to perform these calculations.
Bio
As a computational neuroscientist, Frances Chance has always been fascinated by how neural circuits compute information. Her current research focuses on applying knowledge of how neural systems operate towards the development of neural-inspired algorithms and brain-based architectures.
Frances Chance received her PhD and MS from Brandeis University and her BS from the California Institute of Technology. Currently she is a Principal Member of the Technical Staff at Sandia National Laboratories.
March 22, 2024
Shed Infrared Light on Heat Transfer during Phase Change
Chi Wang, The University of New Mexico
When: Friday, March 22, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Phase change, e.g., boiling and condensation, has been widely used in many industries, such as power plants, electronics cooling, HVAC systems, to transfer heat. Though efficient and easy to implement, its application has been limited due to the lack of fundamental understanding of heat transfer during this process. For example, a phenomenon called boiling crisis, has practically set the upper limit of heat flux that boiling applications can be operated at. At the boiling crisis, a vapor film appears on the boiling surface and isolates part of the boiling surface from the bulk liquid to create a dry patch. The temperature of the boiling surface in this dry patch increases dramatically to a dangerous level as a result of the extremely poor heat transfer characteristic of the vapor film. The heat flux corresponding to the boiling crisis is called critical heat flux (CHF), and the boiling community has been trying to delay the boiling crisis and increase the CHF. Several recent studies have found that surfaces with hydrophilic micro-/nano-structures, such as, nanowires, porous layers, and micropillars, can enhance the CHF substantially. Nevertheless, the physical mechanisms that enable these enhancements remain unclear, mainly due to the lack of proper experimental techniques to elucidate the heat transfer process during the boiling crisis.
In this talk, I will present a new experimental setup that fills this knowledge gap by enabling the first measurement of the temperature and heat flux distributions on micropillar surfaces using infrared (IR) thermometry. Further, I will show how these new measurements help to reveal that some previously ignored factors, i.e., forced convection and transient conduction, contribute more to the CHF enhancements. The new technique also helps to prove that the wicking flow and liquid retention are less important than previously assumed by many studies. In the end, I will show how this new experiment setup is helping us further our understanding of heat transfer during condensation.
Bio
Before joining the Department of Mechanical Engineering at UNM, Dr. Chi Wang was a postdoc in the Department of Mechanical Engineering and Science at the University of Illinois Urbana-Champaign. His research focused on investigating the physical mechanisms of the chilldown process and refrigerant flow boiling as well as heat transfer during condensation with infrared thermometry and endoscopy. Before that, he received his Doctoral degree in Nuclear Engineering from Massachusetts Institute of Technology in 2022. During his doctoral research, he studied the mechanisms of CHF enhancements on nano-engineered surfaces in pool boiling and flow boiling at different pressures. He also contributed to the development of a stochastic model to predict the boiling crisis based on percolation theory. Before joining MIT, he earned his Bachelor’s degree and Master’s degree in Nuclear Engineering from Shanghai Jiao Tong University.
March 8, 2024
Thermomechanics of Propulsion Materials
Georgios Koutsakis, The University of New Mexico
When: Friday, March 8, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Propulsion systems and spacecraft reentry vehicles often use protective coatings for components exposed to extreme thermal conditions because most structural materials cannot withstand high temperatures, and vice versa. In this talk, we will discuss various conductive and radiative heat transfer techniques for different layered insulation structures, e.g., thermal barrier coatings of reciprocating engines, rockets, gas turbines and fiber-based insulations for hypersonic spacecrafts. Fractured-based delamination frameworks will be utilized to assess the structural integrity of thermal barrier coatings and the damage evolution of an ablative thermal protection system under arcjet test conditions.
Bio
Dr. Koutsakis embarked on his independent research as an Assistant Professor at The University of New Mexico in January 2024. During his PhD at the University of Wisconsin-Madison (2017-2022) he developed techniques to maximize in-cylinder heat insulation while ensuring coating durability under the extreme and unsteady conditions of a diesel engine. He was awarded the Society of Automotive Engineers Myers Award for Outstanding Student Paper in 2020. At Harvard University (2022-2023) and University of Virginia (2023) as a Postdoctoral Fellow and Visiting Scientist, respectively, his research focused on characterizing and modeling radiative and conductive heat transfer of high temperature radiation barrier coatings and thermal protection systems for gas turbine and spacecraft applications.
March 1, 2024
Rate-induced tipping in complex high-dimensional ecological networks
Shirin Panahi, Arizona State University
When: Friday, March 1, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided.
Bio
Shirin Panahi received her BS degree in Electrical Engineering at Sadjad University of Technology in 2014, M.S. in 2016, and Ph.D. in 2020 both in bioelectrical engineering at Amirkabir University of Technology. She is currently a postdoctoral scholar in Engineering at Arizona State University. Her current research interests include complex dynamical networks, nonlinear dynamics, chaotic biological modeling, neuroscience, and neural networks.
February 23, 2024
The Muon g-2 Experiment at Fermilab: Precision Measurements and Nonlinear Beam Dynamics
Eremey Valetov, Michigan State University
When: Friday, February 23, 2024, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The Muon g-2 Experiment has achieved a significant milestone by measuring the positive muon anomalous magnetic moment to an unprecedented accuracy of 0.20 ppm, paving the way to a better understanding of particle physics, including beyond-the-Standard-Model possibilities. In the experiment, muons are circulated within a storage ring, and their anomalous precession frequency — the spin precession relative to momentum — is determined from decay positron time and energy data, captured using calorimeters. In support of accurate storage ring simulations, we performed high-order calculations of the field of the experiment's high-voltage quadrupoles. We also carried out accurate, transfer map–based calculations of the storage ring's chromaticity. These calculations are essential for understanding the beam dynamics effects in the Muon g-2 storage ring.
Bio
Eremey Valetov is a researcher with Michigan State University (MSU), specializing in nonlinear beam dynamics, accurate field calculations, and high-precision particle physics experiments. He received his Ph.D. in Physics from MSU in 2017. Eremey is a member of the beam dynamics group of the Muon g-2 Experiment at Fermilab, and he performs lattice design and optimization research for projects at Los Alamos National Laboratory, Paul Scherrer Institute, and Lawrence Berkeley National Laboratory. Eremey's background includes scientific computing, mathematics, and computational mechanics.
February 2, 2024
Stochastic Methods and Adversarial Machine Learning for Autonomous Systems
Hyung-Jin Yoon, Tennessee Tech University
When: Friday, February 2, 2024, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Autonomous systems, such as self-driving cars and unmanned aircraft, encounter considerable uncertainties when navigating complex environments with limited information. Consequently, estimating unobserved states becomes pivotal for effective planning and decision-making in autonomous systems. Additionally, it is crucial to learn the model of the environment to enable adaptation in the face of uncertainty. In the first part of this talk, we introduce the estimation of hidden states using a partially observable Markov decision process (POMDP). We explore related applications such as path-planning for UAVs in proximity and wildfire prediction using satellite imagery. The second part of the talk focuses on using sampling-based optimization for finite time horizon optimal control with vehicle dynamics. We derive a sampling complexity in terms of variance and introduce an adaptive sampling method to balance exploration and exploitation in the control problem. Finally, we introduce an adversarial machine learning method, which involves generating stealthy image attacks on autonomous vehicles utilizing vision-based object detectors to track objects. The aim of adversarial machine learning is to identify vulnerabilities in the perception systems of autonomous vehicles, ultimately allowing us to fortify and enhance the robustness of the entire autonomous system.
Bio
Dr. Hyung-Jin Yoon is an assistant professor in the Department of Mechanical Engineering at Tennessee Technological University of Nevada, Reno. He earned a B.S. in Mechanical Engineering from Hanyang University, South Korea, in 2006 and a Ph.D. in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 2019. From 2006 to 2013, he worked as a Simulation and Test Engineer at Hyundai Motor Company. Dr. Yoon's research interests focus on estimation, decision-making, and cyber-physical systems (CPS). He is particularly interested in decision-making under uncertainties and the trade-off between exploration and exploitation.
January 26, 2024
Safety-Critical Control Under Uncertainties
Pan Zhao, University of Alabama
When: Friday, January 26, 2024, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Efficiently controlling real-world systems subject to model uncertainties while guaranteeing safety is a complex challenge. This problem has been extensively studied in the control literature, including recent advancements in learning-based control. In this talk, I will introduce a few latest developments in safety-critical control of uncertain systems. During the first portion of my talk, I will discuss uncertainty compensation (UC) based approaches, showcasing how UC can be applied to develop effective predictive control schemes capable of handling a broad class of uncertainties. Additionally, I will introduce efficient learning-based control schemes that ensure safety during the learning transients. In the second part of my talk, I will delve into robust approaches, with a focus on the leveraging of contraction theory in designing and learning nonlinear tracking controllers that minimize the effect of uncertainties while providing performance and robustness guarantees.
Bio
Dr. Pan Zhao is an Assistant Professor in the Department of Aerospace Engineering and Mechanics at the University of Alabama (UA). He received his B.E. and M.E. degrees from Beihang University, China, in 2009 and 2012, respectively, and his Ph.D. degree in Mechanical Engineering from the University of British Columbia (UBC), Canada, in 2018. Prior to joining UA, he was a Postdoctoral Researcher in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. His research interests include control and autonomous systems and their applications to aerospace, robotics, and sustainable agricultural management. A current focus of his research group is to design and validate control and decision-making algorithms for autonomous systems to ensure their safe and efficient operation in challenging scenarios under various uncertainties. During his doctoral studies at UBC, he received the Vanier Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council of Canada.
Fall 2023
September 1, 2023
Multirobot Teaming in Adversarial Scenarios
Daigo Shishika, George Mason University
When: Friday, September 1, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
A technological revolution driven by the falling price and increasing performance of hardware has allowed us to envision large scale, complex tasks to be solved by teams of robots. However, even though the barrier to designing autonomous systems have dropped dramatically, there still exist various challenges in realizing swarms of robots that work collaboratively in real world applications. This talk will highlight my research on algorithm design towards enabling robots and autonomous vehicles to perform large scale teaming operations. To explore teaming behaviors, we study adversarial scenarios that induce coordination and cooperation among a group of agents. Specifically in this talk, I will discuss scenarios that are modeled as dynamic game between teams of agents that are relevant to various civilian and military security applications. In designing control algorithms, I introduce a multi-disciplinary approach using tools from dynamics and control, game theory, graph theory, combinatorial optimization, and inspirations from biological systems.
Bio
Daigo Shishika is an assistant professor in the Department of Mechanical Engineering. He obtained his bachelor's degree from the University of Tokyo, Japan, and his master's and PhD from the University of Maryland, College Park, all in Aerospace Engineering. Before joining George Mason University, Daigo was a postdoctoral researcher in the GRASP Laboratory at the University of Pennsylvania. His research interest is in the general area of autonomy, dynamics and controls, and robotics.
September 8, 2023
Extreme Dynamic Tension Physics and Its Applications in the Explosively Driven Fragmentation of Metallic Cylinders
Seokbin (Bin) Lim, New Mexico Tech
When: Friday, September 8, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The fragmentation or facture of metallic cylinders has brought much interests in various engineering applications ranging from the simple static pressure vessel/pipe failure to the military/space/astrophysics applications where the extreme dynamic tension is considered. There has been great improvement in this area of study by many researchers (leaded by the Mott’s works), but the question regarding the fundamental mechanism of the fragmentation patterning originated by the extreme dynamic tension still remains unanswered.
This work herein is targeting to identify the nature of the extreme dynamic tension propagation in a metallic sample, hoping to provide a clue to understand the fragmentation patterning in cylindrical samples during the explosively driven expansion. This work is not aiming to understand the nature of crack formation in micro-structural aspects. Instead, this study will utilize a series of MD code (Molecular Dynamic code, LAMMPS) simulation to examine the applicability/accuracy of the extreme tension theory and to reveal the nature of tension wave propagation.
This work has been supported by FAA (Award#: 15-C-CST-NMT-020), AFRL (Award#: FA8651-23-1-0011).
Bio
Dr. Lim received M.S and Ph.D. degree in the Explosives Engineering program from Missouri S&T. Rolla. MO. His research interest includes, the shock physics, explosives engineering, detonation physics, extreme tension physics. He has been a faculty member in Mechanical Engineering Department at New Mexico Tech since 2006, and is currently leading the explosives engineering program and serving the department as a chair.
September 15, 2023
Non-linear Dynamics and Control to Quantify the Maneuverability of Space Vehicles and Robots
Afroza Shirin, University of Texas at El Paso (UTEP)
When: Friday, September 15, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Determination of safe maneuverable regions of uncrewed systems is a verse area of research. One of the more complicated and expensive problems is quantifying and determining the safe region. In this presentation, a theoretical approach is presented to establish a metric to quantify maneuverability, and a multi-step optimization procedure is proposed to determine the maximum maneuverability of space vehicles and robots.
Bio
Dr. Afroza Shirin is an Assistant Professor in the Aerospace and Mechanical Engineering Department at the University of Texas at El Paso (UTEP) and affiliated as a research faculty in the Aerospace Center. Before joining UTEP, she worked as a Postdoctoral Research Fellow in the Department of Mechanical Engineering and in the Department of Electrical and Computer Engineering at The University of New Mexico. She received her Ph.D. in Mechanical Engineering from The University of New Mexico in 2019. She finished her bachelor's and master's degrees in Mathematics from the University of Dhaka, Bangladesh. Her research experience and expertise are in dynamical systems modeling, data-driven physics-inspired modeling, and diverse design aspects of complex systems using techniques and algorithms from nonlinear dynamics, nonlinear optimal control, optimization, and machine learning. One of her current research interests is data-driven physics-inspired modeling of the dynamics of space vehicles and addressing diverse design aspects under different space considerations. Having versatile and multidisciplinary research experience and interests, Dr. Shirin has recently become involved with the ISRU (In-Situ Resource Utilization) group at the Aerospace Center to develop an ISRU propellants extraction process in Digital Engineering. She is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the American Institute of Aeronautics and Astronautics (AIAA).
September 22, 2023
Representing Respondents in the Production of Official Statistics: The Role of the Survey Methodologist
Alda G. Rivas, Ph.D., Krysten Mesner, Melissa A. Cidade, Ph.D., U.S. Census Bureau
When: Friday, September 22, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Throughout the federal government there are well trained social scientists that act as survey methodologists to ensure that federal surveys are designed in ways that are respondent-centered. These researchers engage a variety of methodologies to solicit feedback from respondents that directly impact the development of survey instruments. In this way, the survey methodologist represents the respondents’ experiences in the production of official statistics. This panel consists of three survey methodologists at the Census Bureau who engage in respondent-centered testing to inform survey design decisions. First, Krysten Messner will provide an overview of cognition and survey instruments, including methodologies and best practices for minimizing bias and maximizing data quality. Then, Alda Rivas will discuss user experience testing in pursuit of intuitive designs for Census Bureau survey interfaces. Finally, Melissa Cidade will consider the future of respondent-centered design, as well as considerations for future federal researchers.
Bios
Alda G. Rivas, Ph.D. is an Interdisciplinary Research Psychologist at the US Census Bureau. She received her Ph.D. in Psychology from Rice University, with a focus on Cognitive Psychology. Since 2017, Alda has applied her knowledge of cognition, experimental design, and data analysis toward the goal of improving the experience of survey respondents and the accuracy of Census data.
Krysten Mesner is a Survey Statistician/Methodologist in the Economic Stastitical Methods Division of the U.S. Census Bureau. Her interest in questionnaire design began in college when she worked as a research assistant in the Questionnaire Design and Research Laboratory at the National Center for Health Statistics. Krysten has been working at the Census Bureau for over 13 years and has worked in both the Demographic Directorate and Economic Directorate conducting cognitive testing on a number of federal survey instruments. She is a graduate of the University of Maryland, College Park.
Melissa A. Cidade, Ph.D. is a Survey Methodologist at the US Census Bureau. She holds a Ph.D. in Sociology from the Department of Sociology at George Mason University, where she also teaches. She specializes in mixed methods social science research design and establishment surveys.
September 29, 2023
Physics-informed Data-Based Safe Reinforcement Learning
Hamidreza Modares, Michigan State University
When: Friday, September 29, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
While the success of reinforcement learning (RL) in computer games has shown impressive engineering feats, unlike computer games, safety-critical settings such as unmanned vehicles must thrash around in the real world, which makes the entire enterprise unpredictable. Standard RL practice generally implants pre-specified performance metrics or objectives into the RL agent to encode the designers’ intention and preferences in achieving different and sometimes conflicting goals (e.g., cost efficiency, safety, speed of response, accuracy, etc.). Optimizing pre-specified performance metrics, however, cannot provide safety and performance guarantees across a wide variety of circumstances that the system might encounter in non-stationary and hostile environments. Besides, safety-critical systems cannot afford to learn from scratch as circumstances advance. In this talk, I will discuss safe RL algorithms that can co-learn performance and safety specifications to proactively resolve conflicts. Besides, RL controllers will be merged with novel data-based safe controllers to ensure deliberation of as much performance as possible safely. To reduce the data requirements for learning, a physics-informed learning approach will learn control policies that conform with both current data as well as the available prior knowledge.
Bio
Hamidreza Modares received a B.S. degree from the University of Tehran, Tehran, Iran, in 2004, an M.S. degree from the Shahrood University of Technology, Shahrood, Iran, in 2006, and the Ph.D. degree from the University of Texas at Arlington, Arlington, TX, USA, in 2015, all in Electrical Engineering. He is currently an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. Prior to joining Michigan State University, he was an Assistant professor in the Department of Electrical Engineering at Missouri University of Science and Technology. His current research interests include reinforcement learning, safe control, machine learning in control, distributed control of multi-agent systems, and robotics. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Systems, Man, and Cybernetics: systems.
October 6, 2023
Physics-Informed Deep Learning for Anomaly Detection and Health Management
George Gorospe
When: Friday, October 6, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Within the field of computational science there are two main branches of research, data-driven machine learning and physics-based numerical modeling. While machine learning relies heavily on the quality and quantity of data depicting the system under investigation, physics-based numerical modeling utilizes fundamental equations, often differential in nature, to describe the characteristics or behavior of the system. With both strengths and weaknesses, these methods are typically considered based on the nature of the problem. Anomaly detection and health management is one such case where both methods may be applicable, but not without some challenges and tradeoffs. Physics-informed deep learning combines these two branches to create a new field, scientific machine learning, enabling physics-based codification of known physical phenomena, such as degradation mechanisms, and data-driven management of model uncertainty. This lecture will introduce and detail important work in hybrid methods, including physics-informed neural networks, physics-informed machine learning, and physics-informed deep learning. Additionally, the lecture will include an example of simple but representative applications tied to current and recent NASA work.
Bio
George Gorospe is a Senior Research Engineer within the Intelligent Systems Division NASA Ames Research Center in Moffett Field, CA. At NASA, George conducts and presents research in the fields of diagnostics, prognostics, and automated test systems. He also drives the development of new technology within the application areas of robotics, aeronautics, and space exploration. George graduated from the UNM School of Engineering in 2012 with a BS in Mechanical Engineering. At UNM George focused on autonomous robotics.
George is currently the manager of the Systems Health Analytics, Resilience, and Physics modeling (SHARP) laboratory at NASA Ames, where he leads the design and development of novel testbeds and automated test systems benefitting the development and maturation of diagnostic, prognostic, and health aware autonomy methods. Additionally, George serves as the technical lead for the Autonomous Systems Enduring Discipline where he manages a research portfolio of projects advancing NASA’s autonomy and autonomous systems capabilities within aeronautics. His research interests include: autonomy, electric propulsion systems, robotic exploration systems, and hardware accelerated computing.
October 13, 2023
Modeling, Estimation, and Analysis of Epidemics over Networks
Philip E. Paré, Purdue University
When: Friday, October 13, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
In this talk, we present and analyze mathematical models for network-dependent spread. We use the analysis to validate a SIS (susceptible-infected-susceptible) model employing John Snow’s classical work on cholera epidemics in London in the 1850’s. Given the demonstrated validity of the model, we discuss several control strategies for mitigating spread, and formulate a tractable antidote administration problem that significantly reduces spread. Then we present a formulation of a parameter estimation problem for an SIR (susceptible-infected-recovered) networked model, where costs are incurred by measuring different nodes' states and the goal is to either minimize the total cost spent on collecting measurements or to optimize the parameter estimates while remaining within a measurement budget. We show that these problems are NP-hard to solve in general and then propose approximation algorithms with performance guarantees. Finally, we conclude by discussing an ongoing project where we are developing online parameter estimation techniques for noisy data and time-varying epidemics.
Bio
Philip E. Paré is the Rita Lane and Norma Fries Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. He received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2018, after which he went to KTH Royal Institute of Technology in Stockholm, Sweden to be a Post-Doctoral Scholar. He received his B.S. in Mathematics with University Honors and his M.S. in Computer Science from Brigham Young University in 2012 and 2014, respectively. He is a recipient of the NSF CAREER award, was an inaugural Societal Impact Fellow at Purdue in 2021, and is a 2023 Teaching for Tomorrow Fellow at Purdue as well. His research focuses on networked control systems, namely modeling, analysis, and control of virus spread over networks.
October 20, 2023
Extended Reality Simulation and Control of Rotorcraft
Umberto Saetti , University of Maryland
When: Friday, October 20, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Simulations of rotorcraft flight dynamics have advanced significantly over the past decade. To provide rapid simulations of generalized maneuvering flight, flight dynamics models were once restricted to relatively low fidelity aeromechanics models. On the other hand, comprehensive aeromechanics simulations historically used much higher fidelity aeromechanics, like free-vortex wake and CFD, at the cost of longer run times. In recent years, increasingly higher fidelity aeromechanics are making their way into flight dynamics simulations, and even real-time piloted simulations. In the meantime, new configurations have become more complex. Future vertical lift (FVL) and urban air mobility (UAM) configurations feature multiple rotors, high levels of aerodynamic interactions, and in the case of UAM, high revolutions-per-minute (RPM) / variable speed rotors. These features drive the need for advanced aeromechanics models while at the same time making real-time speed much more difficult. It is therefore critical that the coupled flight dynamics and high-fidelity aeromechanics models are formulated in such a way, that they can be readily linearized and/or simplified to extract more tractable and less expensive simulation models while still being representative of the physics of interest.
This talk discusses the development of rotorcraft simulations with coupled flight dynamics, state-variable aeromechanics, and aeroacoustics and their integration real-time piloted simulations making use of virtual reality (VR) and motion-base platforms. Extended reality (XR) is achieved through multi-modal cueing in the form of full-body haptics and spatial audio. These are used to both increase simulation immersion and to provide useful information to the pilot on how to control the rotorcraft in degraded and/or denied visual environments. Broader implications and future applications of the methodology are presented in the context of human-machine interaction.
Bio
Dr. Umberto Saetti is an Assistant Professor in the Department of Aerospace Engineering at University of Maryland. Dr. Saetti's research focuses on modeling, simulation, order reduction, and control of the coupled flight dynamics, aeromechanics, and aeroacoustics of vertical lift vehicles. These models are used for studies involving immersive simulations that make use of Extended Reality (XR), human-machine interaction, synthesis of advanced flight control laws, and development of multimodal pilot cueing methods.
Dr. Saetti held appointments as Assistant Professor at Auburn University, as Postdoctoral Fellow in the Daniel Guggenheim School of Aerospace Engineering at Georgia Tech, and as Visiting Scholar at the U.S. Army Aviation Development Directorate at NASA Ames. Dr. Saetti holds a Ph.D. in Aerospace Engineering (with a minor in Computational Science), an M.Sc. in Aerospace Engineering, and an M.Sc. in Electrical Engineering from Pennsylvania State University. He received his B.Sc. in Aerospace Engineering from Politecnico di Milano, Italy. Saetti is an awardee of the 2022 Office of Naval Research Young Investigator Program (ONR YIP) and a recipient of the 2019 Vertical Flight Foundation (VFF) Barnes McCormick Memorial Scholarship.
October 27, 2023
Cracking the code of Failure: Analyzing Historical Examples for Future Successes
Tennille Bernard, John Adam Farris, Mark J. Andrews
When: Friday, October 27, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Discover the transformative power of failure analysis in this riveting panel discussion. Through a captivating exploration of real-life historical examples, including the John Hancock building façade and the Thresher submarine tragedy that ended the life of 129 crew and guests and the grounding of all DC-9 Commercial Passenger Aircraft. Our panelists, former UNM students and experts renowned in their fields, will share their first-hand experiences and the profound impact of failure analysis has on their day-to-day work. From preventing disasters to driving innovation, this event will empower attendees with valuable insights, equipping them to embrace failure as a catalyst for improvement and progress.
Moderator Bio
Tennille Bernard
Tennille is an alumnus of UNM, having graduated with a Bachelor’s and Master's degree from the Mechanical Engineering Department. Her engineering expertise landed her a Senior Combustion Performance Emissions Engineer role at Cummins Inc., where she spearheaded the successful certification of the L9N engine to EPA and California Optional NOx emissions standards, backed by both lab and real-world validation. As a testament to her versatility and adaptability, Tennille transitioned to Cummins' corporate environmental team before earning her MBA, where she demonstrated her ability to navigate complex regulatory requirements and design innovative programs that ensure compliance with emerging global non-emission environmental regulations. Her drive and expertise led her to Amazon, where she developed groundbreaking solutions for compliance challenges related to product movement between the EU and the UK. And as of March 2023, Tennille has brought her impressive talents to American Express, where she is responsible for managing the strategic planning for the entire enterprise data governance and platform portfolio. Tennille's remarkable journey is a testament to her unwavering commitment to excellence and her ability to thrive in any challenge that comes her way.
Panelist Bios
John Adam Farris
Born in Albuquerque, New Mexico & educated in ABQ Public Schools & U of NM with a 1954 BS degree in Mechanical Engineering. Served two-years in the USAF in Dayton, Ohio doing R&D for aircraft hydraulic systems during the Korean War. His job included reading all USAF aircraft accident reports & writing new or updating existing hydraulic & pneumatic component specifications to correct any deficiencies. Worked 41+ years for Pall Corp, mostly on Long Island, NY, in various engineering, sales, marketing & administrative capacities including being president of a Pall subsidiary. He consulted for Pall for another four years. He holds two patents in advanced filtration hardware. He retired as a Pall Corporate VP. While at Pall he lectured over most of the world on how to prevent catastrophic failures and how to prevent fine dirt (silt) from causing abrasive wear in lubricated bearings & hydraulic systems. He is a member of the UNM/Mechanical Engineering Advisory Board & he also serves as a judge for the UNM/ME Senior Design class. He resides in Albuquerque, New Mexico.
Mark J. Andrews
Mark J. Andrews has been a member of the Mechanical Engineering Advisory Council since 2013. He has a BS in Mechanical Engineering from the University of New Mexico (1991), and M.S. (1992), and Ph.D. in Mechanical Engineering from the New Mexico State University (2000). Mark’s engineering career began while attending UNM which included co-op work assignments with the GE Aircraft Engines and summer positions at the Sandia National Laboratory. In 1999, Mark postponed his Ph.D. studies and accepted employment with the US Dept. of Energy’s Technical Development Leadership Program where he completed his Ph.D. degree from NMSU in 2000. Mark spent most of his engineering career at Caterpillar Inc. where he began as the Senior Research Engineer and Manager of DoE funded programs to develop ceramic materials for diesel engine applications. Mark also worked as an Engineering Specialist managing Caterpillar’s Corporate Quality and Reliability software, and as a Senior Specialist in Virtual Product Development. Mark was selected to lead a new technical program in the Virtual Product Division to quantify the uncertainties for modeling and simulation analyses. After retiring from Caterpillar in 2015, Mark became the UQ Technology Steward at SmartUQ a leader in the development of software for quantifying simulation and data analytic uncertainties, retiring again in 2019.
November 3, 2023
Extreme High Temperature Integrated Circuits For Harsh Environment Applications – Scaling of SiC CMOS Technology
Zhong Chen, University of Arkansas
When: Friday, November 3, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Silicon carbide (SiC) is a wide band gap semiconductor material with superior properties. Due to its fewer dislocation defects and its ability to form native oxides, this material possesses an advantage among wide band gap materials. Despite having these superior properties its low voltage application is less explored. Complementary metal-oxide-semiconductor (CMOS) technology is extremely important in integrated circuit (IC) areas. Silicon (Si) is the dominant player in it for the decades where scaling has contributed a major role in this flourishment. The channel length of commercial Si CMOS devices in modern ICs has reached 3 nm whereas SiC is still in the micrometer range. Therefore, SiC CMOS technology is still in its infancy which can be compared with Silicon technology in the mid-1980s range. When the scaling of SiC devices enter the sub-micron and deep submicron range, proper device design and device processing are necessary to reveal the benefit of scaling. In this talk, the challenges and recent achievements of SiC CMOS scaling will be presented and discussed.
Bio
Dr. Zhong Chen is an associate professor with the University of Arkansas (UA). He is a faculty member affiliated with High-Density Electronic Center (HiDEC) at UA. Further, he is the Faculty Director of the Multi-User Silicon Carbide (MUSiC) National Research and Fabrication Facility that is currently under construction at the UA. He worked on device and integrated circuit robustness, ESD protection and reliability design at Texas Instruments at Dallas, TX from 2009-2016. He has extensive experience in semiconductor device processing, reliability, ESD design and failure analysis. His research interests are in SiC device fabrication, integrated circuits processing, high temperature device and power packaging.
November 10, 2023
Near-field optical microscopy as a tool for materials characterization and design in nanoscale systems
Andrew Jones, Center for Integrated Nanotechnologies at Los Alamos National Laboratory
When: Friday, November 10, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Optically coupled scanning probe microscopy techniques provide a means of investigating material properties with resolution far below the diffraction limits imposed by conventional far-field optics. Here, I will provide an introduction scattering scanning near-field optical microscopy techniques which utilize sharp, nanometer-scale, scanning probes coupled to optical excitation as a means of performing nano-spectroscopic material identification with resolution lengths 10s-100s of times higher than traditional optical microscopes. This technique provides a means of identifying and mapping of material composition and heterogeneity within nanoscale systems and surfaces without the need for labeling or placing samples within vacuum environments. Such information can be highly valuable as a means of identifying how local, nanometer material composition, optical, and electronic structure can impact the larger scale performance of systems and devices. I will provide some illustrative examples of how near-field optical microscopy techniques are currently being leveraged at the Center for Integrated Nanotechnologies in Los Alamos to study the nano-optical response of individual nanostructures and materials. Finally, I will discuss how we are extending these techniques for the sensing of optical forces and the study of a novel form of optical surface waves in layered dielectric surfaces known as Bloch Surface Waves.
Bio
Andrew Jones is a staff scientist at the Center for Integrated Nanotechnologies at Los Alamos National Laboratory. His research focus primarily lies in the combined use of scanning probe and optical microscopy techniques to extract information about the intrinsic electrical and optical properties of nanoscale materials. This information is leveraged to better understand the behavior of complex materials for future microelectronics and optical/quantum computing applications. His research includes the development and implementation of novel, nano-optical scanning probe microscopy techniques and the development of ultrafast microscopy techniques for the characterization of quantum emitters for computing and sensing applications.
November 17, 2023
Analyzing Dynamics of Networked Systems Using Graph Signal Processing: Case Studies on Smart Grids
Wenbin Wan, The University of New Mexico
When: Friday, November 17, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The recent decade has been critical in designing and deploying cyber-physical systems (CPS). CPS security and CPS safety often are essential. Our research aims to enable safe operation for CPS subject to significant uncertainties, such as malicious attacks, unforeseen environments, and model uncertainties, by integrating resilient estimation algorithms and safe control methods. First, we consider the problem of a safety-constrained control architecture design against GPS spoofing/jamming attacks. We develop a resilient estimation algorithm to detect attacks and design control algorithms based on the model predictive controller subject to limited sensor availability due to the sensor attacks. In another scenario of actuator attacks, we propose a constrained attack-resilient estimation algorithm (CARE). The proposed CARE performs better in estimation and attack detection by reducing estimation errors, covariances, and false negative rates. Following that, we extend our resilient estimation algorithm to a spatio-temporal framework. Building on the proposed resilient spatio-temporal filtering, we design a proactive adaptation architecture for connected vehicles in unforeseen environments, synthesizing techniques in spatio-temporal data fusion and robust adaptive control. Finally, we propose an efficient interval estimation method for estimating systems under faulty model uncertainties. The method applies to a broad class of systems with a large uncertainty setup.
Bio
Wenbin Wan is an Assistant Professor in the Mechanical Engineering Department at UNM. Wenbin received his Ph.D. in Mechanical Engineering and M.S. in Applied Mathematics from UIUC. He earned a B.Sc. degree in Mechanical Engineering from the University of Missouri-Columbia. He was appointed MechSE Teaching Fellow at UIUC and named CPS Rising Star by the University of Virginia. Wenbin’s research interests are in control and estimation, optimization, machine learning, and cyber-physical systems and their safety-critical applications.
Spring 2023
January 27, 2023
Bringing in the Bystander: A prevention workshop for establishing a culture of responsibility in STEM academic contexts
Dr. Heng E. Zuo, Dr. Mala Htun (Special Advisor to the SOE Dean for Inclusion and Climate, also professor of political science at UNM)
When: Friday, January 27, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
BITB is an evidence-based prevention program, developed at the University of New Hampshire, that is currently implemented in over 600 colleges and universities throughout North America as well as in the U.S. military. It is based on the concept that all community members have a role to play in ending incivility, harassment, racialized microaggressions, and discrimination. The important role that a bystander has in intervention is the basis for this unique and effective program. During the workshop, we will go over the elements of the Bringing in the Bystander ® (BITB) curriculum. The program will help participants gain the knowledge and skills needed to identify and safely intervene before, during, and after instances of harmful behaviors.
After the workshops, we will invite all participants to answer an incentive survey. The main research question for Dr. Mala’s team is: What are the effects of participating in bystander intervention curriculum on student attitudes, behaviors, and intentions to persist in engineering?
February 3, 2023
Advanced Research on Exascale Simulation for Multiphysics Problems
Vinod Kumar, Ph.D., The University of Texas at El Paso
When: Friday, February 3, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Understanding complex physics and physical phenomena of intricate engineering systems is critical for designing and maintaining reliable, efficient, and economic systems that can be safely operated. System-level engineering challenges are often complex and involve multi-physics coupling of multiple scales in several orders of magnitude. The latest advancement in computational and data-driven technologies with supercomputing interfaces/infrastructures promises novel ways to address intricate engineering challenges. However, effective implementation of these technologies requires a workforce educated with an in-depth understanding of multi-physics concepts from multiple disciplines & cross-cutting technologies. In this presentation, we will discuss issues, challenges, needs, and opportunities for advanced modeling & simulation (M&S) as an investigation tool for system level design and analysis. In particular, we will also highlight our workforce development efforts through the Rio Grande Consortium for Advanced Research on Exascale Simulation (UNM - Lead, UTEP, NMSU, NMT, PVAMU & Sandia) through interdisciplinary research and curriculum developments. The presentation will be concluded with results and discussion on boundary layer transition analysis on Ogive at Mach 6, melt-infusion in B4C microstructures at pore-scale, physics informed AI/ML framework.
Bio
Dr. Kumar’s research goal is to develop and integrate cutting-edge computational tools for complex engineering and science challenges by leveraging Exascale/High Performance Computing (HPC), machine learning and artificial intelligence, data analytics/bigdata concepts, Uncertainty Quantification (UQ), and leading-edge computational capabilities. His research group has focused on real-life and fundamental thermal-fluid applications coupled with crosscutting domains such as structure dynamics, solid mechanics, biomedical engineering, hypersonic. The group uses Computational Fluid Dynamics (CFD) and Fluid-Structure Interactions (FSI) with HPC algorithms on massively parallel supercomputers. Research activities include multiple interdisciplinary projects including developing flow conductance model porous core at pore level for CO2 sequestration, studying effects nanoparticles/coatings on thermal energy storage systems in Concentrating Solar Power (CSP) system, and FSI analysis Deep Venous Thrombosis (DVT), CFD-Discrete Element Modeling (CFD-DEM)/Multiphase Simulations, laser propagation characterization for Air Force Remote Sensing/Directed Energy applications, exascale computing, biomass gasification, long-term weather forecast, turbulence modeling, and FSI analysis of flexible membranes.
February 10, 2023
Equivariant system identification using recurrent reservoir computers
Dr. Fredy Vides, Universidad Nacional Autónoma de Honduras
When: Friday, February 10, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
In this talk, some general results in structured matrix approximation theory with applications to autoregressive representation of recurrent reservoir computers are presented. Firstly, a generic nonlinear time delay embedding is considered for the time series data sampled from a system under study. Secondly, sparse least-squares and subspace rotation methods are applied to identify approximate representations of the output coupling matrices, that determine the autoregressive representations of the recurrent reservoir computers corresponding to some given system under consideration. Prototypical algorithms based on the aforementioned techniques, together with some applications to approximate identification and predictive simulation of equivariant nonlinear systems, that may or may not exhibit chaotic behavior are outlined.
February 17, 2023
A stochastic model for cascading failures in power grids: capturing the interdependencies among power flow, communication, and human-operator performance
Majeed Hayat, Marquette
When: Friday, February 17, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Despite the rapid increase in the automation of monitoring and control of modern power grids, human operators continue to play a crucial role in the reliable operation of the grid. However, the operators’ actions can be non-optimal due to various factors affecting their performance, which are dependent on both the operators’ conditions and the nature of the decisions and actions to be made. Here, we review an analytic model, based on Markov chains, for predicting the dynamics of cascading failures in power grids, following initial transmission-line failures, while capturing the effects of operators’ behavior, as quantified by the probability of human error under various circumstances.
February 24, 2023
Machine Learning for Detecting Internet Traffic Anomalies
Ljiljana Trajkovic, Simon Fraser University
When: Friday, February 24, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Border Gateway Protocol (BGP) enables the Internet data routing. BGP anomalies may affect the Internet connectivity and cause routing disconnections, route flaps, and oscillations. Hence, detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze BGP update messages collected from RIPE and Route Views collection sites. Survey of supervised and semi-supervised machine learning algorithms for detecting BGP anomalies and intrusions is presented. Deep learning, broad learning, and gradient boosting decision tree algorithms are evaluated by creating models using collected datasets that contain Internet worms, power outages, and ransomware events.
Bio
Biography: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director and President of the IEEE Systems, Man, and Cybernetics Society and the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society, a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society, and a Fellow of the IEEE.
March 3, 2023
Variational approach to porous media: thermodynamics, muscle action and irreversible processes
Vakhtang Putkaradze, University of Alberta
When: Friday, March 3, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Porous media presents a highly complex example of fluid-structure interactions where a deforming elastic matrix interacts with the fluid. Many biological organisms are comprised of deformable porous media, with additional complexity of a muscle acting on the matrix. Using geometric variational methods, we derive the equations of motion of a for the dynamics of both the passive and active porous media. The use of variational methods allows to incorporate both the muscle action and incompressibility of the fluid and the elastic matrix in a consistent, rigorous framework. We also derive conservation laws for the motion, perform numerical simulations and show the possibility of self-propulsion of a biological organism due to particular running wave-like application of the muscle stress. We also discuss variational derivation for equations of porous media from the point of view of variational thermodynamics, leading to conclusions about thermodynamically consistent functional forms of friction forces and stresses acting on the media. We conclude by deriving the equations for porous media that is damaged by the applied stress, and discuss possible applications and particular cases.
March 10, 2023
Unveiling the Mechanisms of Cellular Phenotypes through Molecular Profiling
Dr. Wai Lim Ku, NIH
When: Friday, March 10, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
In this talk, I will share my research on the mechanisms of defining and quantifying cellular phenotypes through molecular profiling. I will explain how gene expression synchrony among single cells can be used to generate cellular phenotypes and how dynamical models of coupled chemical oscillators can measure the stability of these phenotypes. I will also provide evidence on pre-disease states and compare the effectiveness of co-expression analysis of single-cells to traditional gene expression analysis for identifying the underlying phenotypic genes. Finally, I will discuss the role of histone modification in establishing a stable phenotype.
March 24, 2023
Advances in Fast Model Predictive Control and Constrained Model Reference Adaptive Control for Aerial Robotics Applications
Andrea L’Afflitto, Virginia Tech
When: Friday, March 24, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Unmanned aerial vehicles (UAVs) can provide invaluable support to law enforcement agencies involved in complex and potentially hazardous operations, such as surveying unknown and potentially hostile environments and installing sensors in remote locations. Enabling autonomy for these complex missions requires creating new theoretical results, which are also computationally efficient. In this seminar, Dr. L’Afflitto will present a unique optimization-based guidance system for autonomous UAVs tasked with operating in hostile environments without any prior knowledge of the location and capabilities of external threats. Successively, our speaker will present the first extension of the model reference adaptive control architecture to switched dynamical systems within the Carathéodory and the Filippov framework. The applicability of these results will be shown by numerical simulations and flight tests involving multi-rotor UAVs.
Bio
Dr. Andrea L’Afflitto is an associate professor with the Grado Department of Industrial and Systems Engineering and an affiliate professor with the Departments of Aerospace and Ocean Engineering and Mechanical Engineering and the National Security Institute at Virginia Tech. He received B.S. and M.S. degrees in aerospace engineering from the University of Napoli ``Federico II,'' an M.S. degree in mathematics from Virginia Tech in 2010, and a Ph.D. degree in aerospace engineering from Georgia Tech in 2015. His current research interests include nonlinear robust control, optimal control, and the design of control systems for unmanned aerial systems. Presently, Dr. L’Afflitto serves as the Senior Editor of the Autonomous Systems area for the IEEE Transactions on Aerospace and Electronic Systems. He is the recipient of several academic awards, including the DARPA Young Faculty Award in 2018.
March 31, 2023
Connecting Equivariant Bifurcation Theory with Engineering Applications
Antonio Palacios, San Diego State University
When: Friday, March 31, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The advent of novel engineered or smart materials, whose properties can be significantly altered in a controlled fashion by external stimuli, has stimulated the design and fabrication of smaller, faster, and more energy-efficient devices. As the need for even more powerful technologies grows, networks have become popular alternatives to advance the fundamental limits of performance of individual devices. Thus, in the first part of this talk we provide an overview of twenty years of work aimed at combining ideas and methods from equivariant bifurcation theory to model, analyze and fabricate novel technologies such as: ultra-sensitive, low-power, magnetic and electric field sensors; networks of nano oscillators; multi-frequency converters, and central pattern generator networks for animal gaits.
In the second part of the talk, we discuss more recent work on networks of precision timing devices and feedforward networks for signal amplification.
April 7, 2023
A Robust Control Approach to Analysis of Neural Network Driven Dynamical Systems
Mahyar Fazlyab, Johns Hopkins University
When: Friday, April 7, 2023, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Going beyond machine learning tasks, neural networks also arise in a variety of control and robotics problems, where they function as feedback control policies, motion planners, perception modules/observers, or models of dynamical systems. However, the adoption of these approaches in safety-critical domains (such as robots working alongside humans) has been hampered due to a lack of stability and safety guarantees, which can be largely attributed to the large-scale and compositional structure of neural networks. These challenges only exacerbate when neural networks are integrated into feedback loops, in which time evolution adds another axis of complexity. In this talk, we present a novel framework based on non-(convex) optimization and robust control that can provide certificates of stability, safety, and robustness for NN-driven systems.
Bio
Mahyar Fazlyab is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University (JHU) since July 2021. Dr. Fazlyab received his Ph.D. in Electrical and Systems Engineering (ESE) from the University of Pennsylvania (UPenn) in 2018. Dr. Fazylab’s research interests are at the intersection of optimization, control, and machine learning. His current research focus is on the safety and stability of learning-enabled autonomous systems. Dr. Fazlyab won the Joseph and Rosaline Wolf Best Doctoral Dissertation Award in 2019 from the ESE department at UPenn.
April 14, 2023
Complex field-reversal dynamics in nanomagnetic systems
Michael Saccone, Los Alamos National Lab
When: Friday, April 14, 2023, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The broad field of metamaterials uses nanoscale fabrication techniques to create systems with collective behaviors that mimic or go beyond those exhibited in natural materials. The innate properties of metamaterial "atoms" differ from literal atoms and molecules, often introducing second order kinetic behaviors that give rise to new complexity. Nanomagnetic materials, which started as analogies to frustrated magnetism built from thin, patterned films of ferromagnetic materials, are now being considered for device physics which depend heavily on the systemic dynamics and coupling to external magnetic field. A recent model of how nanomagnetic domains flip, the Glauber mean-field model, is used here to understand how systems of nanomagnets flip directions when opposed by external field. This reversal is solvable on one dimensional chains and trees at zero temperature, the cascade of spin flips giving rise to harmonic power spectra. The same cascades in two and three dimensions form fractal clusters whose shape depends on the strength of the field and the tuning of interactions between nanomagnets. The results suggest experiments in physical nanomagnets that can produce fractal structures which are potentially useful to neuromorphic applications and could better explain recent results in pyrochlore magnetic systems that suggest fractal structures emerge in the dynamics.
Bio
Michael Saccone began his academic journey at Cabrillo College, a community college overlooking the Monterey Bay. Encouraged by the enthusiasm of his instructions, he continued on to the University of Alaska Fairbanks to complete his undergraduate education in physics. He then returned to the Monterey Bay to attain his doctorate in physics from the University of California Santa Cruz, where he worked to advance the fundamental statistical physics of artificial spin ice. He now works with two outstanding theoreticians, Cristiano Nisoli and Francesco Caravelli, at Los Alamos National Lab.
April 21, 2023
Data-driven modeling of fish behavior: a multisensory feedback control approach
Daniel A. Burbano Lombana, Rutgers University
When: Friday, April 21, 2023, 3:30 PM
Please note this seminar will be on Zoom.
Zoom link: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Millions of years of evolution have endowed animals with refined and elegant mechanisms to orient and navigate complex environments. Elucidating the underpinnings of these processes is of critical importance not only in biology to understand migration and survival but also for engineered network systems to aid the development of bio-inspired algorithms for estimation and control. Particularly interesting is fish rheotaxis, which is the innate ability of fish to orient and swim against a current. Empirical evidence suggests that rheotaxis is a multi-sensory process integrating multiple cues, such as vision and hydrodynamics. Little is known, however, about the information pathways and the integration process underlying this complex behavior. This talk will discuss a novel data-driven mathematical model of adult zebrafish swimming in a flow, which contributes insight into the mechanisms underlying rheotaxis. The mathematical model leverages potential flow theory, stochastic differential equations, and control theory to describe rheotaxis as a multisensory feedback control process. Experimental data on adult zebrafish swimming in a water tunnel is used to calibrate the model and validate its predictive power. The model reveals that a simple yet effective hydromechanical feedback mechanism plays a critical role during rheotaxis. The results suggest that integrating data-driven research and dynamical system theory constitutes a viable approach to understanding the mechanisms underlying animal behavior and paves the way towards designing bio-inspired control solutions for the next generation of engineered network systems.
Bio
Daniel A. Burbano Lombana is an assistant professor in the Department of Electrical and Computer Engineering at Rutgers University. His research interests include modeling, inference, and control of complex systems with particular attention to problems in data-driven modeling of behavior, bio-inspired control, collective animal behavior, and distributed network systems.Previous to this appointment he was a Provost Faculty Fellow in the Department of Mechanical and Aerospace Engineering at New York University, NY (2022), and a postdoctoral associate at Northwestern University, IL (2019). He received his Ph.D. in Computer and Control Engineering from the University of Naples Federico II, Italy (2015), and a M.S.c degree in Industrial Automation from the National University of Colombia (2012).
Fall 2022
September 9, 2022
Integrated Sensing and Communications Using Chaotic Systems
Chandra S. Pappu, Union College
When: Friday, September 9, 2022, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Chaotic oscillators with nonlinear behavior have tremendous potential in signal processing. We consider a higher dimensional chaotic oscillator with fast and slow time scales. The slow oscillating part of the system is used to sweep the fast oscillating part, thereby generating a waveform that changes its frequency as a function of time. We show the potential of this quasi-FM waveform for joint radar sensing and communication (RadComm) systems. The digital information is encoded using the chaos frequency shift keying (CFSK) approach. A drive-response synchronization scheme is utilized to decode the information. Results indicate that our proposed signal design closely matches the bit-error rate (BER) of theoretical noncoherent frequency shift keying (FSK). Furthermore, since the receiver is based on a self-synchronizing chaotic system, making for fast synchronization, it is robust to timing errors or Doppler shifts. We also show that the proposed waveform has good autocorrelation and a near thumbtack ambiguity function, desirable for high-resolution radar imaging.
Bio
Chandra S. Pappu is an Assistant Professor in the Department of Electrical, Computer, and Biomedical Engineering at Union College. He received his M.S. and Ph.D. from the University of Texas at El Paso in 2010 and 2015, respectively. His current research interests include chaotic systems, radar signal processing, and joint radar communication systems.
September 16, 2022
Research and Educational Opportunities at UNM’s MTTC Cleanroom Enabling Microsystems and Nano Fabrication / Multidisciplinary Research Opportunities in Microsystems for: BS, MS, and PhD students
Matthias Pliel and Nathan Jackson, UNM
When: Friday, September 16, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Pleil abstract
This presentation will cover opportunities for researchers and their students available at The University of New Mexico’s Manufacturing Training and Technology Center (MTTC). Dr. Pleil will provide an overview of fabrication and metrology equipment available, courses taught, NSF projects supported, and a bit about the Manufacturing Engineering Master’s Degree Program. You will be provided a list of available equipment, and what it can provide, bringing added value to your research and learning.
Jackson abstract
The aim of my talk is to relay to students the potential research opportunities that are available in my group. I’m currently seeking multidisciplinary students with interests in: 1) developing novel thin film flexible/stretchable multifunctional materials, 2) biomedical microsystem device development, 3) development of propulsion micro-thruster devices, 4) vibration energy harvesting, and 5) atomizers etc…. Microelectromechanical Systems (MEMS) are widely used in everyday applications, such as telecommunications (RF filters), accelerometers, gyroscopes, microphones, ultrasound, digital micromirrors, optical shutters, and numerous other applications. Research into enhancing device performance, developing new materials, and discovering new applications has significant societal and academic impact. The talk will also give a high-level overview of ongoing and future research opportunities within my group.
Pleil biography
Matthias Pleil, Ph.D. is the Principal Investigator for the Support Center for Microsystems Education (SCME, 2017) a continuation of the Southwest Center for Microsystems Education (2004). He is a Research Professor of Mechanical Engineering, MTTC Cleanroom Manager and Director of the Manufacturing Engineering Program at the University of New Mexico. He teaches undergraduate and graduate engineering courses and promotes micro and nanotechnology fabrication. Previously he was a faculty member at Central New Mexico Community College in both the Schools of Applied Technologies and Math, Science and Engineering (MSE). He has over 12 years of combined experience in Semiconductor Manufacturing from both Texas Instruments and Philips Semiconductors, where he worked as a Senior Process and Equipment Engineer, and Engineering Manager in Photolithography, Yield, and Metrology. Dr. Pleil received his Ph.D. in Physics in 1993 from Texas Tech University, where he completed original research on Time-Resolved Fluorescence Spectroscopy.
Jackson biography
Nathan Jackson is an Assistant Professor in the Mechanical Engineering Department at UNM. He is also the Director of the Nanoscience and Microsystems Engineering Program and Associate Director of the Manufacturing Engineering Program. He received his Ph.D in Biomedical Engineering from Arizona State University. Prior to UNM he worked at a microelectronics research institute (Tyndall National Institute) located in Cork, Ireland as a Senior Researcher and head of the PiezoMEMS team. His research interests are in novel microfabrication methods, MEMS, BioMEMS, thin film piezoelectrics, advanced manufacturing, and flexible/stretchable materials. He has developed MEMS devices for vibration energy harvesters, particle sensors, atomizers, acoustic resonators, robotics, tactile sensors, and ultrasound transducers. He is a senior member of IEEE and has published more than 90 peer-reviewed journal publications and has 10 patents focused on MEMS and functional materials.
September 23, 2022
Statistics of Attractor Embeddings in Reservoir Computing
Louis M. Pecora
When: Friday, September 23, 2022, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Louis M. Pecora and Thomas L. Carroll
Code 6392, US Naval Research Laboratory
Washington, DC 20375, US
A recent branch of AI or Neural Networks that can handle time-varying signals often in real time has emerged as a new direction for signal analysis. These dynamical systems are usually referred to as reservoir computers. A central question in the operation of these systems is why a reservoir computer (RC), driven by only one time series from a driving or source system of many time-dependent components, can be trained to recreate all dynamical time series signals from the drive leads to the idea that the RC must be internally recreating the drive dynamics or attractor. This has led to the possibility that the RC is creating an embedding of the drive attractor in the RC dynamics. There have been some mathematical advances that move that argument closer to a general theorem. However, for RCs constructed from actual physical systems like interacting lasers or analog circuits, the RC dynamics may not be known well or known at all. And many of the existing embedding theorems have restrictive assumptions on the dynamics. We first show that the best way to analyze RC behavior is to first treat it properly like a dynamical system, which it is. This will lead to some conflict with existing ideas about RCs, but also a clarification of those ideas. Secondly, in the absence of complete theories on RCs and attractor embeddings, we show several ways to analyze the RC behavior to help understand what underlying processes are in place, especially regarding if there are good embeddings of the drive system in the RC. We show that a statistic we developed for other uses can help test for homeomorphisms between a drive system and the RC by using the time series from both systems. This statistic is called the continuity statistic and it is modeled on the mathematical definition of a continuous function. We show the interplay of dynamical quantities (e.g. Lyapunov exponents, Kaplan-Yorke dimensions, generalized synchronization, etc.) and embeddings as exposed by the continuity statistic and other statistics based on ideas from nonlinear dynamical systems theory. These viewpoints and results lead to a clarification of various currently vague concepts about RCs, such as fading memory, stability, and types of dynamics that are useful.
September 30, 2022
Projective embeddings of dynamical systems
Francesco Caravelli, LANL
When: Friday, September 30, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
There has been a lot of interest, over the past years, in machine learning techniques such as Deep Learning, and in neuromorphic circuits based on nanoscale components such as memristors. Often, training a neural network goes through the difficult task of finding minima of non-convex functions, and a common technique used for this purpose is gradient descent. However, as gradient descent does not guarantee finding the absolute minimum, but a local one.
The present talk discusses novel proposals, inspired by the exact dynamics of memristors, to embed gradient descent into higher dimensional spaces. While this implies a higher computational cost, we suggest that such embedding leads to the modification of the spectral properties of the non-convex function, in a way that rigid barriers (maxima) disappear. We discuss the advantages and disadvantages of this technique and the connection to recent results in neuromorphic devices.
Based on
[1] F. Caravelli, F. C. Sheldon, F. L. Traversa, Science Advances 52, 7 (2021)
[2] F. Caravelli, F. L. Traversa, M. Bonnin, F. Bonani, https://arxiv.org/abs/2201.02355
October 7, 2022
Fiber optic sensing for structural health monitoring
Joe Hart, US Naval Research Lab
When: Friday, October 7, 2022, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Optical fiber is widely associated with telecommunications. However, some of the same qualities that have led to the success of fiber optics in telecommunications--for example, small footprint, wavelength multiplexing, and immunity to electromagnetic interference--have also enabled optical fiber to excel as a sensor. In this talk, we will introduce several fiber optic sensing modalities and discuss their utility for structural health monitoring of marine platforms.
Bio
Joe Hart is a Research Scientist at the US Naval Research Lab in Washington, DC. He joined NRL in 2018 as a Karles Fellow in the Fiber Photonics Section. He received his Ph.D. in 2018 from the University of Maryland in nonlinear dynamics in optical systems, with a focus on constructing laboratory experiments for the study of coupled oscillator networks. His current research interests include using photonic crystal and fiber photonics as a tool for sensing, neuromorphic computing, and random number generation.
October 21, 2022
Femtosecond laser micromachining for stress-based figure correction of thin mirrors
Dr. Heng E. Zuo
When: Friday, October 21, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The fabrication of many high-resolution thin-shell mirrors for future space telescopes remains challenging, especially for revolutionary mission concepts like NASA’s Lynx X-ray Surveyor. It is generally harder to fabricate thin mirrors to the exact shape than thicker ones, and the coatings deposited onto mirror surfaces to increase the reflectivity typically have high intrinsic stress which deforms the mirrors further. Since the rapid development of femtosecond laser technologies over the last few decades have triggered wide applications in materials processing, we have developed a novel mirror figure correction and stress compensation method using a femtosecond laser micromachining technique for stress-based surface shaping of thin-shell x-ray optics. We employ a femtosecond laser to selectively remove regions of a stressed film that is grown onto the back surface of the mirror, to modify the stress states of the mirror.
In this talk, I will present experimental results to create both isotropic and anisotropic stress states on thin flat silicon mirrors with thermal oxide (SiO2) films using femtosecond lasers. We have shown that equibiaxial stress can be generated through uniformly micromachined holes, while non-equibiaxial stress arises from the ablation of equally spaced troughs. I will also present results from strength tests to show how this process minimally affects the strength of mirrors. These developments are beneficial to the high throughput correction of thin-shell mirrors for future space-based X-ray telescopes.
October 28, 2022
Resonance-based mechanisms of generation of oscillations in networks of non-oscillatory neurons
Horacio G. Rotstein, NJIT
When: Friday, October 28, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations, and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonator’s resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to the so-called firing rate models with adaption. Our results have direct implications for neuronal network oscillations in more complex systems and other biological oscillatory networks (e.g., biochemical, genetic).
November 4, 2022
Analysis and Control of Functional Brain Networks
Fabio Pasqualetti, Riverside
When: Friday, November 4, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
During a cognitively demanding task or at rest, the brain exhibits a rich repertoire of large-scale functional patterns. These patterns are a measure of the coherence among the neural activities in different brain areas, reflect different cognitive functions, and can also be used as biomarkers for different psychiatric and neurological disorders. For example, while patterns of transient and partial coherence have been observed in healthy individuals, increased coherence in neural systems is often associated with degenerative diseases including Parkinson’s and Huntington’s disease, and epilepsy.
In this talk, I will discuss methods to model, analyze, and control functional patterns in oscillatory brain networks. I will start by modeling the rhythmic activity of a neural system as the output of a network of diffusively-coupled heterogeneous oscillators, and use different synchronization notions as a proxy for functional patterns. I will derive conditions for the emergence of cluster synchronization, where independent groups of synchronized oscillators coexist in a network, and compare such conditions against empirical brain data. Finally, I will present a method to enforce desired synchronization patterns through minimally invasive and localized changes to the network structure, validate some of our findings using a well-accepted neurovascular model, and discuss future research directions.
Bio
Fabio Pasqualetti is a Professor of Mechanical Engineering at the University of California, Riverside. He completed a Doctor of Philosophy degree in Mechanical Engineering at the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree (M.Sc. equivalent) in Automation Engineering at the University of Pisa, Italy, in 2007, and a Laurea degree (B.Sc. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2004. He is the recipient of the 2017 Young Investigator Award from the Army Research Office and the 2019 Young Investigator Research Award from the Air Force Office of Scientific Research. His articles received the 2016 TCNS Outstanding Paper Award, the 2019 ACC Best Student Paper Award, the 2020 Control Systems Letters Outstanding Paper Award, the 2020 Roberto Tempo Best CDC Paper Award, and the 2021 O. Hugo Schuck Best Paper Award. His main research interests are in the areas of network systems, computational neuroscience, and machine learning.
November 18, 2022
Biomechanics of neural interfaces and neuromodulation
Jit Muthuswamy, Arizona State University
When: Friday, November 18, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Neural interfaces are foundational tools that enable us to test hypotheses of brain function and dysfunction and are also critical to the success of a variety of emerging prosthetic devices. In the central nervous system, there is an increasing thrust to monitor hundreds and thousands of neurons simultaneously and also to increase the “quality” of the neural information. Non-invasive imaging approaches, while exciting and promising, are still at an infancy. Majority of the effort so far has been devoted towards millimeter scale and microscale engineering at the neural interface. This talk will focus on some of the biomechanical challenges in the acute and chronic phase that impact reliability and stability of implantable neural interfaces. These include tissue remodeling around the interface due to foreign body response and how this might influence cellular function at the interface. I will make the case that understanding the mechanical issues at the interface is key to the design of reliable chronic neural implants. Further, I will also discuss some potential molecular and cellular mechanisms involved in modulating the function of neurons through ultrasonic pressure waves. On the abiotic end of the neural interface, we will discuss technology development challenges in biomaterials and packaging & interconnects. This talk will discuss engineering approaches that have been developed and tested at the Neural Microsystems laboratory in Arizona State University, Tempe, AZ to address the above challenges.
Bio
Jit Muthuswamy is currently an Associate Professor in Biomedical Engineering in the School of Biological and Health Systems Engineering and an affiliate faculty in Electrical Engineering at Arizona State University, Tempe, AZ. After an undergraduate degree in Electronics and Electrical Communication Eng. from Indian Institute of Technology, Kharagpur (India), he obtained his Masters degree in Electrical Engineering and a Masters degree in Biomedical Engineering and a PhD in Biomedical Engineering, all from Rensselaer Polytechnic Institute, Troy, NY. He did his post-doctoral fellowship in Biomedical Engineering at Johns Hopkins University, Baltimore, MD. His research program in Neural Interfaces, Neuromodulation and high-throughput biochip platforms have been supported by NIH, NSF, Flinn foundation, Whitaker foundation, DARPA, and the Arizona Biomedical Research Commission. He is a senior member of the IEEE and Associate Editor of PLoS ONE and Frontiers in Neuroscience. He won the Excellence in Neural Engineering award at the Joint International conference of the IEEE Engineering and Medicine Society and Biomedical Engineering Society (BMES).
December 2, 2022
Machine Learning for Classifying Anomalies and Intrusions in Communication Networks
Zhida Li
When: Friday, December 2, 2022, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
Cyber attacks are becoming more sophisticated and, hence, more difficult to detect. Using efficient and effective machine learning techniques to detect network anomalies and intrusions is an important aspect of cyber security. A variety of machine learning models have been employed to help detect malicious intentions of network users. In this talk, I will first present applications of machine learning techniques for classifying network anomalies such as Internet worms, denial of service attacks, power outages, and ransomware attacks. I will then introduce the variable features broad learning systems (BLSs) developed by our research group. They offer desired performance with shorter training time compared to deep neural networks. Furthermore, a border gateway protocol anomaly detection tool BGPGuard will be presented.
Bio: Zhida Li received the B.E. and M.Eng.Sc. degrees in electrical engineering and microelectronic design from the University College Cork, Ireland, respectively. He received his Ph.D. degree in engineering science from Simon Fraser University, Canada where he is currently a postdoctoral fellow. His current research interests include the development of fast machine-learning algorithms and real-time systems for detecting network anomalies.
December 9, 2022
Eco-evolutionary dynamics: A game theoretical approach
Sayantan Nag Chowdhury, UC Davis
When: Friday, December 9, 2022, 3:30 PM
Zoom: https://unm.zoom.us/j/5409824881
Those who would like more information should contact Professor Francesco Sorrentino: fsorrent@unm.edu
Abstract
The emergence and abundance of cooperation in the context of the Darwinian theory of evolution pose a challenge to date. To overcome this formidable challenge, scientists often resort to Evolutionary Game Theory as a common mathematical framework and games such as the prisoner's dilemma and the snowdrift game as metaphors for studying cooperation between unrelated individuals. On the other hand, the concurrence of ecological and evolutionary processes often arises as an integral part of various biological and social systems. Studying a mathematical model that considers both holds promise of insightful discoveries related to the dynamics of cooperation. We upgrade the contemporary multigame by introducing punishment as an additional strategy in addition to the traditional cooperators and defectors. Punishers bear an additional cost from their own resources to try and discourage or prohibit free-riding from selfish defectors. We also incorporate the ecological signature of free space, which has an altruistic-like impact because it allows others to replicate and potentially thrive. I will discuss how this proposed model can offer the individual dominance of cooperators and defectors as well as a plethora of mixed states, where different strategies coexist, followed by maintaining diversity in a socio-ecological framework. In particular, our model reports the simultaneous presence of different subpopulations through the spontaneous emergence of cyclic dominance, and we determine various stationary points using traditional game-theoretic concepts and stability analysis.
Spring 2022
April 29, 2022
Hypersonic Aerothermal Analysis – Past, Present, and Future
Dr. Paul Delgado, Sandia National Lab
When: Friday, April 29, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
April 22, 2022
Bio-inspired design of small rotors
Prof. Svetlana Poroseva, The University of New Mexico
When: Friday, April 22, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
April 15, 2022
Density-Aware Control of Autonomous Multi-Agent Systems
Dr. Kooktae Lee, New Mexico Institute of Mining and Technology
When: Friday, April 15, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
April 08, 2022
Beyond Li-ion: High Energy Metal-based Batteries
Dr. Shuya Wei, The University of New Mexico
When: Friday, April 08, 2022, 3:30 PM
Where: It will be in person, room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
April 01, 2022
Pushing boundaries: fractures in low permeability media
Dr. Emilie Dressaire, University of California, Santa Barbara
When: Friday, April 01, 2022, 3:30 PM
Where: It will be on ZOOM.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
March 25, 2022
Interactional Aerodynamics Research at the Intersection of Wind Energy & Rotorcraft
Dr. Sven Schmitz, The Pennsylvania State University
When: Friday, March 25, 2022, 3:30 PM
Where: It will be on ZOOM.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
March 11, 2022
Multiphase CFD: Risks, Rewards, and Remorse
Prof. Wayne Strasser, Liberty University
When: Friday, March 11, 2022, 3:30 PM
Where: It will be on ZOOM.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
March 4, 2022
High Energy Laser Beam Control – Aero-Optics and Opto-Mechanics at its Best
Dr. Nicholas J. Morley, Kirtland AFRL
When: Friday, March 4, 2022, 3:30 PM
Where: In person at 3:30 p.m. in room 218 of the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
February 25, 2022
Designing efficient biomimetic propulsors with distributed actuation
Prof. Alexander Alexeev, Georgia Tech
When: Friday, February 25, 2022, 3:30 PM
Where: The seminar will be on Zoom at 3:30 p.m.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
February 18, 2022
Learning CFD and Simulation to Boost your Career
Dr. Gilles Eggenspieler , ANSYS
When: Friday, February 18, 2022, 3:30 PM
Where: IN PERSON at 3:30 p.m. in room 218 of the ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
February 11, 2022
Materials & Multi-Scale Patterning Approaches to Inspire Printed Electronics Solutions
Mr. Adam W. Cook, Sandia National Laboratories
When: Friday, February 11, 2022, 3:30 PM
Where: This will be on ZOOM.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
February 4, 2022
Computational Modeling Strategies for Energy Transport Applications
Prof. Francine Battaglia, University at Buffalo, The State University of New York
When: Friday, February 4, 2022, 3:30 PM
Where: This seminar will be IN PERSON in room 218 in the UNM ME Bldg.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
January 28, 2022
Two-dimensional Silicon Carbide: An Emerging Semiconducting Material
Dr. Sakineh Chabi, The University of New Mexico, Mechanical Engineering
When: Friday, January 28, 2022, 3:30 PM
Where: The seminar will be on zoom.
Those who would like more information should contact Professor Poroseva: poroseva@unm.edu
December 3, 2021
Build a road to space through fluid analysis of rocket engines
Dr. Matthieu Masquelet
When: Friday, December 3, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 19, 2021
Hydrodynamic Instabilities & turbulence: A journey through scales
Dr. Ye Zhou, Lawrence Livermore National Laboratory
When: Friday, November 19, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 12, 2021
Laminar flow control through surface roughness
Prof. Sharon O. Stephen, University of Sydney, Australia
When: Friday, November 12, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 5, 2021
Evaluation of CFD as a Surrogate for Wind Tunnel Testing at High Supersonic Speeds
Dr. Marie Denison, NASA Ames Research Center
When: Friday, November 5, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 29, 2021
Computational design and control of stochastic gene expression in single cells
Dr. Zachary R. Fox, Los Alamos National Lab
When: Friday, October 29, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 22, 2021
Input-Output Stability: Bringing a Landscape of Theorems to the Control of Networks
Dr. Leila Bridgeman, Duke University
When: Friday, October 22, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 8, 2021
Multiscale Modeling of General Grain Boundaries (GBs): From Computing GB Diagrams via Atomistic Simulation to Machine Learning to GB Transition
Dr. Chongze Hu, Sandia National Laboratory
When: Friday, October 8, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 1, 2021
Magnetoconvection in a horizontal duct flow at very high Hartmann and Grashof numbers
Ruslan Akhmedagaev, University of Michigan - Dearborn
When: Friday, October 1, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 24, 2021
Spacecraft Station Keeping and Re-Entry Targeting using Model Predictive Control
Dr. Ryan Caverly, the University of Minnesota, Twin Cities
When: Friday, September 24, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 17, 2021
High-Performance Scientific Computing Research, Education, and Support at UNM
Prof. Patrick Bridges, UNM CARC
When: Friday, September 17, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 10, 2021
Design, development, deployment, and sustainment of Launch Vehicle Systems from a Logistics Perspective
Mr. David Hollis, retired Systems Engineer/Logistics
When: Friday, September 10, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 3, 2021
Instabilities in liquid metal batteries
Prof. Oleg Zikanov, University of Michigan Dearborn
When: Friday, September 3, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
April 30, 2021
Direct Simulation of Deformation Instabilities in Film-Substrate Structures Using Embedded Imperfections
Siavash Nikravesh, Ph.D. candidate, UNM Department of Mechanical Engineering
When: Friday, April 30, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
April 23, 2021
High-Fidelity Multidisciplinary Analysis and Optimization Framework for Rotorcraft Applications
Dr. Boris Diskin, National Institute of Aerospace
When: Friday, April 23, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
April 16, 2021
Verification and Validation with Uncertainty Quantification is the Scientific Method for Computational Science
Dr. William J. Rider, Sandia National Laboratories
When: Friday, April 16, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
April 9, 2021
Theoretical and Experimental Basis for the Super Dielectric Model of Dielectric Materials
Dr. Jonathan Phillips
When: Friday, April 9, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
April 2, 2021
Bioinspiration, Biomimetics and Drones
Dr. Mostafa Hassanalian, Assistant professor, Department of Mechanical Engineering, New Mexico Tech
When: Friday, April 2, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
March 26, 2021
Machine learning boosted modeling and simulation of process, structure and property in additive manufacturing
Zhuo Wang, Ph.D. student at the University of Michigan-Dearborn
When: Friday, March 26, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
March 12, 2021
On importance of learning and questioning scientific dogmas with application to direct numerical simulations of incompressible turbulent flows
Professor Svetlana V. Poroseva, UNM Department of Mechanical Engineering
When: Friday, March 12, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
March 5, 2021
Liquid Cooling of IT Equipment
Dr. Jessica Gullbrand, Intel
When: Friday, March 5, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
February 26, 2021
Advanced Manufacturing and Materials Design: Research Opportunities in Mechanical Engineering at UNM
Dr. Pankaj Kumar, Assistant Professor, Mechanical Engineering, UNM
And
Theory of Reinforcement Learning and Its Practice in Robotics and Assistive Devices
Dr. Ali Heydari, Assistant Professor, Mechanical Engineering, UNM
When: Friday, February 26, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
February 19, 2021
High-pressure turbulent jet flows and non-dissipative methods for complex domains
Nek Sharan, Postdoctoral Research Associate, Los Alamos National Laboratory
When: Friday, February 19, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
February 12, 2021
Directed Energy: State-of-the-Art & Future Research Challenges
Dr. Nicholas J Morley, AFRL
When: Friday, February 12, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
February 5, 2021
Machine learning for combustion applications in the exascale era
Dr. Marc Henry de Frahan, NREL
When: Friday, February 5, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
January 29, 2021
LDRD-DR Project: Hot Smoke-Dust Signatures to Predict Nuclear Fallout and Winter
Dr. Eunmo Koo, Los Alamos National Lab
When: Friday, January 29, 2021, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 20, 2020
Modeling and Simulation of COVID-19 Spreading as Aerosol Transport in a Closed Environment: Classroom as an Example
Mohamed Abuhegazy, Ph.D. candidate, UNM Mechanical Engineering
When: Friday, November 20, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 13, 2020
Co-Simulation Approach for Dynamic Analysis of Power and Energy System
Dr. Mayank Panwar, National Renewable Energy Laboratory, Golden, CO
When: Friday, November 13, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
November 6, 2020
Modeling and Simulation of Turbulent Flows for Aerospace Applications
Dr. Brian R. Smith, Lockheed Martin Aeronautics Company
When: Friday, November 6, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 30, 2020
Modular Robotics for Autonomous In-Space Assembly
Dr. John R. Cooper, NASA Langley Research Center
When: Friday, October 30, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 23, 2020
Aeroacoustics of reconnecting vortices
Mr. Hamid Mohammad Mirzaie Daryan, University of Waterloo, Canada
When: Friday, October 23, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 16, 2020
Simulations of the Shock-Driven Kelvin-Helmholtz Instability with FIESTA
Brian Romero, Ph.D. candidate, UNM ME Department
When: Friday, October 16, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 9, 2020
Low-Speed Wind Tunnel Design and The Zia Initiative
Dr. Paul M. Delgado, Sandia National Laboratories
When: Friday, October 9, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
October 2, 2020
An Optimization Perspective on Trust and Trustworthiness in Autonomous Systems
Dr. Natalia Alexandrov, NASA Langley RC
When: Friday, October 2, 2020, 3:30 PM
Abstract: The application domain of the work described in this talk is the near-to-far-future airspace, where the projected density and heterogeneity of autonomous participants, including non-cooperative agents, combine to increase system complexity and uncertainty, with ensuing threats to safety. Given the increased complexity, control of airspace will have to transition to human-machine teams, with the ever-rising authority of autonomous systems (AS). The growing use of AS leads to a potential paradox: AS are meant to address system uncertainty; however, machine authority and human-machine interactions are themselves major sources of uncertainty in the system. Because trustworthiness and trust are connected to decision making, which, in turn, is an optimization problem, subject to expressed and unexpressed constraints, in this presentation, we examine the nature of the attendant optimization problems, discuss some approaches to solutions, as well as persistent gaps.
Biography: Dr. Natalia Alexandrov works at the NASA Langley Research Center. Her interests are in multidisciplinary methods for variable-fidelity modeling, problem synthesis, design optimization (MDO), and control of complex cyber-physical-human systems, including mechanical artifacts and heterogeneous adaptive systems, such as future transportation systems and biological systems; concepts of trust and trustworthiness in systems governed by autonomous computational intelligence, such as machine-learning-based decision making, and human-machine teams with a high degree of machine autonomy.
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 25, 2020
Grid integration of renewable energy resources – Challenges and how to address them
Dr. S M Shafiul Alam, Idaho National Laboratory
When: Friday, September 25, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 18, 2020
Deep Learning for Autonomous Systems at NASA Langley Research Center
James E. Ecker, NASA Langley Research Center
When: Friday, September 18, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 11, 2020
Leading-Order Analysis by Artificial Intelligence
Dr. Bryan Kaiser, Los Alamos National Lab
When: Friday, September 11, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
September 4, 2020
Talk title 1: Multidisciplinary Research Opportunities for BS, MS, and PhD students in Micro-Electro-Mechanical Systems laboratory
Talk title 2: Motion Planning and Model Predictive Control at UNM
Nathan Jackson PhD, SMIEEE, Assistant Professor, The University of New Mexico and Claus Danielson, PhD, Assistant Professor, The University of New Mexico
When: Friday, September 4, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
August 28, 2020
Radiation Dosimetry of Inhaled Radioactive Aerosols: CFPD and MCNP Transport Simulations of Radionuclides in the Lung
Khaled Talaat, a Ph.D. candidate in the UNM Nuclear Engineering Department
When: Friday, August 28, 2020, 3:30 PM
This is a Zoom presentation and those outside of the UNM Mechanical Engineering Department who wish to attend should contact Professor Poroseva for the link: poroseva@unm.edu
March 6, 2020
Structural Optimization Subjected to Stochastic Dynamic Loading
Dr. Jiaqi Xu, Department of Civil, Construction & Environmental Engineering, The University of New Mexico
When: Friday, March 6, 2020, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Structural optimization has a long history of being applied in engineering design as an alternative to the traditional trial-and-error design method. Many of the lateral forces that must be considered in structural design (e.g., wind and earthquake) are stochastic in nature. Simplifying the random dynamic loading to be equivalent static loads is convenient; nevertheless, neglecting the stochastic dynamic nature of the lateral loads will result in sub-optimal designs. Dr. Xu proposed a method incorporating the stochastic character of the excitation into the optimization procedure directly, which can concurrently consider both structural safety and serviceability. The proposed optimization method in this thesis can be employed for structural optimization of both linear and nonlinear structures; subjected to both stationary and non-stationary stochastic dynamic loading; considering both single-variate and multi-variate stochastic process. Theoretical formulas were established and application process was proposed based on the state space equations for structural optimization subjected to stochastic dynamic loading. Generalized pattern search algorithm was employed to conduct structural optimization. Applicability and realizability of the proposed optimization method were verified via the illustrative examples of low-, mid-, high-, and super high-rise structures subjected to wind and seismic excitation. Besides linear elastic structures, the proposed optimization method can be also employed for nonlinear structures with hysteretic behavior, e.g. buckling restrained braced frame (BRBF).
About the Speaker
Jiaqi Xu is a postdoctoral fellow in the Department of Civil, Construction & Environmental Engineering at The University of New Mexico. She was a visiting Ph.D. student in the University of Illinois at Urbana-Champaign (UIUC) in 2014~2016. She received her Ph.D. degree at Tongji University in 2017. Utilizing her knowledge in structural optimization, she has worked on various research topics, including structural stiffness optimization, topology optimization, structural analysis, etc. Her ongoing research is focused on Augmented Reality (AR) applications for structural engineering and structural health monitoring.
February 21, 2020
Research Opportunities in Mechanical Engineering
Svetlana Porosova, Associate Professor, Mechanical Engineering, UNM and Nathan Jackson, Assistant Professor, Mechanical Engineering, UNM
When: Friday, February 21, 2020, 3:30 - 4:30 PM
Where: MECH 218
About the Speakers
Dr. Svetlana Poroseva is an associate professor at the Department of Mechanical Engineering, University of New Mexico. She has also the courtesy appointment at the UNM Department of Mathematics and Statistics and is affiliated with the UNM Centers for Advanced Research Computing and Emerging Energy Technologies. She holds Ph.D. degree in the fluid and plasma mechanics and M.S. degree in physics, both from the Novosibirsk State University in Russia. Prior joining UNM, she was affiliated with the Center for Turbulence Research at the Stanford University, Aerospace Engineering Department at the Texas A&M University, the Center for Advanced Power Systems and the School of Computational Science at the Florida State University, and Institutes of Theoretical and Applied Mechanics and Thermophysics at the Siberian Branch of Russian Academy of Sciences. Dr. Poroseva is an associate fellow of AIAA, a member of the AIAA Fluid Dynamics Technical Committee and the Turbulence Model Benchmarking Working Group. She is also a member of APS DFD and an honorary member of the Pi Tau Sigma Society. At UNM, she is the faculty advisor for the UNM AIAA Student Branch.
Dr. Nathan Jackson is an Assistant Professor in the Mechanical Engineering Department at UNM. He received his Ph.D in Biomedical Engineering from Arizona State University. Prior to UNM he worked at a microelectronics research institute (Tyndall National Institute) located in Cork, Ireland as a Senior Researcher and head of the PiezoMEMS team. His research interests are in the area of novel microfabrication methods, MEMS, BioMEMS, piezoelectrics, smart materials, neural interfaces, advanced manufacturing, and flexible/stretchable materials/devices. He has developed MEMS devices for vibration energy harvesters, particle sensors, atomizers, acoustic resonators, robotics, tactile sensors, and ultrasound transducers. He a technical committee member for IEEE MEMS, SPIE Microtechnologies, E-MRS, and IEEE NANO conferences. He is a senior member of IEEE and has published more than 70 peer reviewed journal publications focused on MEMS and functional materials. He has 10 patents licensed to various companies, and he was a finalist for inventor of the year in Ireland in 2016.
February 14, 2020
Conjugated Polymer Composites for Biologically Inspired Sensing and Energy Storage/Conversion Systems
Michael Freund, Harry Shirreff Professor of Chemical Research, Dalhousie University
When: Friday, February 14, 2020, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Conjugated polymers are an exciting class of materials that hold great promise in emerging electronic, sensing and energy applications. The excitement surrounding the field has resulted from the tremendous possibilities presented by merging the vast knowledge base of synthetic organic chemistry and polymer science with critically important areas of electronic materials and solid-state physics. This rapidly growing field presents opportunities for revolutionizing material science and electronics in ways we are just beginning to imagine. This presentation will discuss the development of conjugated polymers for use in artificial photosynthesis and artificial olfaction, inspired by biological systems. In particular, recent developments in the design of membranes consisting of electronically and ionically conducting polymers will be discussed including their figures of merit and engineering challenges for use in coupling the absorption of light with the generation of solar fuels. In the area of artificial olfaction, the development of chemically diverse conjugated polymer sensing elements compatible with CMOS integrated circuits will be described.
About the Speaker
Dr. Freund was born in Gainesville Florida in 1964. He received a B.S. Degree in Chemistry from Florida Atlantic University in 1987. Dr. Freund received his Ph.D. in 1992 from the University of Florida. Subsequently, he became a Postdoctoral Fellow in the Department of Chemistry at the California Institute of Technology where his research contributions helped to establish a multi-investigator interdisciplinary research program on the development olfactory-inspired sensor arrays. He began his academic career as an Assistant Professor of Chemistry at Lehigh University before moving back to Caltech as the Director of the Materials Science Center in the Beckman Institute. In 2002, he moved to the University of Manitoba where he attained the rank of Professor in the Department of Chemistry and Tier 1 Canada Research Chair in Electronic Materials. During his thirteen years at the University of Manitoba he has been either lead or co-PI on projects securing over 30 million dollars in research and infrastructure funding through federal and regional funding sources, which he leveraged to establish the Manitoba Institute for Materials as Director. He recently joined the faculty at Dalhousie University where he is Harry Shirreff Professor of Chemical Research and Director of the Clean Technologies Research Institute. Dr. Freund has published over 110 articles with over 6000 citations and has been issued 28 US and 15 international patents.
February 7, 2020
Achieving Distributive Control for Soft and Musculoskeletal Robots
Dr. Ed Habtour, Soft & Compliant Robots, Sandia National Laboratories, Assistant Professor Candidate
When: Friday, February 7, 2020, 3:30 - 4:30 PM
Where: MECH 218
Abstract
The presentation describes the design, modeling, and development of dynamical structures with muscle-inspired materials which emulate the musculoskeletal behavior of biological systems. The goal is to expandboth the dynamics and materials design space beyond the traditional structural performance objectives, such as mass, strength and stiffness, in engineered systems. The presentation discusses a proposed design strategy for emulating living structures with dynamic functions, which consists of two main steps (i) geometrically modulating and segmenting materials to maximize range of motion; and (ii) enabling distributive actuation and sensing for the segmented elements with active soft materials (muscles). Theproposed design is intentionally activating nonlinearities to control the performance attributes of dynamical living structures. A new model is developed to gain insight into the connections in time-varying nonlinearities, such as geometric, material, kinematic, and force. Preliminary results, and control strategies are provided. Finally, the presentation outlines future research activities for: (i) discovering novel dynamic behaviors instigated by nonlinearities across the micro-to macro-scales; (ii) solving emerging challenges related to modeling, scaling, and controls; and (iii) transitioning to useful applications.
About the Speaker
Dr. Ed Habtour is a Principle Member at Sandia National Laboratories in New Mexico, U.S.A. Prior to joining Sandia, Ed held technical positions at Swales Aerospace, Northrop Grumman, US Army Materiel System Activity Analysis and Army Research Laboratory (ARL). He was a visiting scientist at University of Twente, the Netherlands. His research focuses on identifying and studying innate nonlinear interactions in observed in dynamical systems. The overarching goal of the research is to engineer structures with distributive control and novel dynamics for applications in aerospace, robotics, and energy. Ed earned a BS in mechanical engineering from Utah State University, three MS degrees from Johns Hopkins, Purdue and University of Maryland (UMD), and a Ph.D. in mechanical engineering from UMD. Ed is an Associate Editor for the Journal of Nondestructive Evaluation, Diagnostics & Prognostics, and has served in many international committees and panels. He has published over seventy journal and conference papers. He received several awards for his technical accomplishments including the US Dept. of the Army Commander’s Medal and Achievement Award, ARL Science Award, and IEEE Evans/P.K. McElroy Award.
January 31, 2020
Theory of Reinforcement Learning & Its Practice in Robotics & Assistive Devises
Dr. Ali Heydari, Robotics & Cyber-Physical Systems, Lyle School of Engineering, Southern Methodist University, Assistant Professor Candidate
When: Friday, January 31, 2020, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Control plays the role of enabler in mechanisms in which, a parameter “changes”. For decades, a controller design was deemed successful, when the desired motion/change was achieved. However, today, the standards are much higher. “Qualities” including low energy consumption for a better range, human friendliness for safe and efficient interactions, high accuracy and productivity, high robustness to uncertainties and imperfections, and small footprint on environment are important “requirements” now.
Adaptive Dynamic Programming (ADP), also called Reinforcement Learning (RL), has a great potential to win in these new domains. The reason is, ADP/RL is motivated by nature, that is, the perfect way humans learn to operate machinery and control mechanisms. As an “intelligent control” tool, however, ADP/RL has been subject to shortcomings both in terms of its “rigor” (guarantees of desired performance) and its “scalability” (possibility of extension to challenging problems, beyond toy examples). An overview of my past and future research activities on resolving these two deficiencies will be presented in the seminar. Moreover, applications of the developed methods in challenging problems in robotics will be briefly discussed, including human-machine interaction and co-design of robotic mechanisms and their controllers.
About the Speaker
Ali Heydari received his B.S. and M.S. degrees from Sharif University of Technology, Iran, in 2005 and 2008, respectively, and his Ph.D. degree from the Missouri University of Science and Technology, Rolla, Missouri, in 2013. He is currently an assistant professor of mechanical engineering at the Southern Methodist University, Dallas, Texas. He is the first or sole author of more than 20 journal papers. His research is mainly focused on mathematical analysis of Adaptive Dynamic Programming (sponsored by the National Science Foundation) and also on its applications in robotics. He serves on the editorial boards of IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology. He is also a member of the Technical Committee on Aerospace Controls with the IEEE Control Systems Society and the Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning with the IEEE Computational Intelligence Society.
December 06, 2019
Statistical Inference and Model Selection using Efficient Sampling Algorithms on Next-generation, Single Cell Gene Expression Data
Dr. Yen Ting Lin, Los Alamos National Laboratories
When: Friday, December 06, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
In this talk, I will first introduce an experimental technique—single-molecule RNA fluorescent in situ hybridization (sm RNA FISH)—which measures transcribed mRNA and the discrete state of activation in a single cell, and provides a “snapshot' of the stochastic process of gene expression. Then, I will discuss how we use a class of coarse-grained stochastic models, formulated as continuous-time and individual-based chemical reactions in a well-mixed environment, to infer kinetic properties of stochastic gene expression from the experimental data. I will present our developed accurate sampling procedure to efficiently solve the problem numerically (up to 1000-fold speed-up compared to conventional algorithms). The increased efficiency permits us to go beyond standard fitting procedures and enter to the realm of statistical inference. In the final part of the talk, I will present a high-level description of how we carry out the full-scale Bayesian analysis on our continuous-time probabilistic models using data from discrete-time observations. The outcome of the analysis, the uncertainty quantification of the parameters and model structures, will be presented.
Reference: Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts, Journal of Chemical Physics 151, 024106 (2019)
About the Speaker
Yen Ting is a physicist interested in a diverse spectrum of problems in nonlinear dynamics, stochastic processes, and non-equilibrium statistical physics and their applications to biology, ecology, epidemiology, and fluid dynamics. Currently, he is a staff scientist at the Information Sciences Group, Computer, Computational and Statistical Sciences Division (CCS-3), Los Alamos National Laboratory.
November 22, 2019
A tour of Electromechanical Non‐linear Materials
Joe T. Evans, Jr., President, Radiant Technologies Albuquerque, New Mexico
When: Friday, November 22, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Advances in the well-being of human beings occur when new materials or their means of manufacturing arise. A new technology that may lay the foundation for a giant step forward is the piezoelectrically-driven machine large and small. Some machines are already well known: medical ultrasound, sonar, thermal security sensors, IR cameras, and knock sensors in an engine. Less well known but nevertheless ubiquitous are ferroelectric integrated circuit memories (FRAM) and piezoelectric inkjet printers. Far more complex piezoMEMS hover on the horizon, perhaps becoming as numerous microprocessors today. Mechanical engineers will be vital to the success of this technology. Joe Evans has worked in this field since the first FRAM was fabricated in Room O-29 in Farris Engineering Center here in Albuquerque in 1987. He will give a tour of the properties and applications of non-linear materials, especially those with electrical properties, and attempt to forecast the near future of this technology.
About the Speaker
Joe T. Evans, Jr. is the President of Radiant Technologies in Albuquerque. (www.ferrodevices.com) Radiant manufactures non-linear electrical test equipment for measuring dielectric, paraelectric, ferroelectric, piezoelectric, pyroelectric, magnetoelectric, and cryogenic properties of capacitors. The company also fabricates integrated thin-film piezoelectric and ferroelectric products, one of the first companies in the world to do so. The devices are embedded in the test equipment as reference devices for researchers. Joe earned a Bachelor of Science in Electrical Engineering as a Distinguished Graduate from the United States Air Force Academy in 1976. After completing Undergraduate Pilot Training in Oklahoma, Joe served as a flight instructor in Oklahoma in the supersonic T-38 Talon. The US Air Force sponsored him at Stanford University, where he earned a Master of Science in Electrical Engineering. Joe was subsequently assigned to the Air Force Weapons Laboratory in Albuquerque, NM. He left the Air Force in 1984 and co-founded Krysalis Corporation, becoming the first to create thin ferroelectric films on silicon substrates and subsequently fabricating the world’s first fully functional CMOS ferroelectric random access memory in 1987. All commercial FRAMs to date use the architecture of that first ferroelectric IC. Joe co-founded Radiant Technologies, Inc. in 1988 where he continues to work on a variety of issues in ferroelectric materials including FRAM, ferroelectric-gate transistors, ferroelectric capacitor reliability, and piezoelectric MEMs. His present goal is to create a useful form factor for thin-ferroelectric-film capacitors so engineers can insert these devices into circuits and ICs.
November 15, 2019
Memristive circuits
Francesco Caravelli, Los Alamos National Laboratory
When: Friday, November 15, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Nanoscale components often exhibit memory, both quantum and classical. Resistors are no exception (already at the classical level) and the use of memristors for a variety of purposes is currently under scrutiny. Applications range from machine learning on chip to compact and passive memory devices using crossbar arrays. In this talk we discuss the nitty-gritty mathematical aspects of memristive circuits in the analog regime (no CMOS), their connection to the Ising model, and plausible future directions beyond crossbars.
November 8, 2019
Multifunctional Materials for Self‐Powered Sensing and Energy Harvesting
Donghyeon Ryu, Assistant Professor, Mechanical Engineering, New Mexico Tech
When: Friday, November 8, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
In this seminar, the speaker will present his research effort to design multifunctional materials that help realize autonomous structural systems – for instance, autonomous damage detection, self-healing, and energy harvesting. Recently, mechano-luminescence-optoelectronic (MLO) composites were designed for realizing self-sustainable structural systems capable of self-powered sensing and energy harvesting. The self-sustainable structural systems, in which MLO composites are built, are envisioned to sense external stimuli to autonomously detect damage without human intervention. In addition, energy being dissipated in infrastructures could be harvested as a supplemental energy source for powering the sensor network as well as structural systems. The MLO composites are composed of two functional building blocks: 1) mechano-optoelectronic (MO) conjugated polymer and 2) mechano-luminescent (ML) phosphor. The functional building blocks are designed to attain target functionalities for scaling up from molecular to device scale. The MLO composites generate direct current (DC) through two-step mechanical-radiant-electrical energy conversion. It was shown that the DC varied its magnitude with tensile strain level and loading frequency. The generated DC can be used as a supplemental energy source. Potential applications of the MLO composites can be self-powered internet-of-things (IoT) sensors, sensing skin for non-contact diagnosis and prognosis, autonomous composites (AutoCom) for self-sustainable infrastructures, and wearable devices for monitoring human motions.
About the Speaker
Donghyeon Ryu, Ph.D., P.E., is an assistant professor in the Department of Mechanical Engineering at New Mexico Tech (August 2014 – present). He obtained a Ph.D. in the Department of Civil and Environmental Engineering in September 2014 and M.S. in the Department of Mechanical and Aerospace Engineering in March 2014 from the University of California, Davis. Before then, he obtained M.S. (2008) and B.S. (2004) in the Department of Civil and Environmental Engineering at Yonsei University in Seoul, South Korea. Dr. Ryu is active in research on multifunctional materials and nanocomposites for autonomous infrastructures, structural health monitoring, multi-modal sensors, and energy harvesting. His research has been funded by NASA, Office of Naval Research, Federal Aviation Administration, Los Alamos National Lab, among some others. He won three best paper awards from America Society of Mechanical Engineers, 9th International Workshop on Structural Health Monitoring, and 10th International Conference on DamageAssessment of Structures. He was an ASCE ExCEEd Teaching Workshop Fellow in 2018. He has edited 1 book series, authored 3 book chapters, 14 journal papers, and 26 conference papers.
November 1, 2019
Fluid mechanics and thermodynamics of fluids under extreme conditions
Daniel T. Banuti, Assistant Professor, Mechanical Engineering, The University of New Mexico
When: Friday, November 1, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
In the pursuit of more efficient combustion systems, pressures in rocket engines, Diesel engines, and gas turbines have reached 'supercritical' conditions where our everyday experience with fluids, often divided into incompressible liquids and ideal gases, is no longer valid: instead, droplets no longer exist, and fluids are simultaneously more compressible than ideal gases and have a higher heat capacity than liquids. The talk will outline recent findings in supercritical fluids and their relevance for propulsion and sustainable energy. The new concepts of a distributed supercritical latent heat and 'pseudo-boiling' are discussed, and it will be shown how this insight allowed to finally explain an injection experiment after more than a decade. Supercritical conditions in energy systems are here and they are here to stay - now we need to find out how this affects next generation design.
About the Speaker
Dr. Banuti joined the faculty of The University of New Mexico in August 2019 after postdoctoral positions at Caltech / NASA Jet Propulsion Laboratory and Stanford's Center for Turbulence Research (CTR). He held Research Scientist positions at the Silicon Valley CTR spin-off Cascade Technologies, and the German Aerospace Center (DLR), Institute of Aerodynamics and Flow Technology, Spacecraft Department in Göttingen, with a focus on numerical research on combustion and injection in rocket engines, and hypersonic flow / flow control.
October 18, 2019
Control Energy of Complex Networks
Isaac Klickstein, Ph.D. Candidate, Mechanical Engineering, The University of New Mexico
When: Friday, October 18, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Complex networks are everywhere from the electricity we use supplied by the power grid to the roads we drive on, from the social media we use every day to the neurons communicating in your head as you read this abstract. Our ability to describe these networks and model the dynamics that govern their behavior has improved greatly in recent years and so attention has turned to controlling, or influencing, them. The control energy, or control effort, required to perform a particular task or achieve a certain goal is an important quantity when designing a controller and recently, for complex networks, it has been shown to span many orders of magnitude. Analysis is performed on both network models and datasets from many scientific fields and some design problems are discussed that remain open. This presentation covers the span of my research into the underlying mechanisms that cause this large variation of control energy of complex networks using tools from control theory, optimal control, optimization, and statistical mechanics.
About the Speaker
Isaac Klickstein received his BSME from UNM in 2015, and was a member of the 2015 FSAE team. He is currently a PhD candidate in the Department of Mechanical Engineering and is planning to defend his dissertation later this semester. He has published papers on controls and network science in journals such as Nature Communications and Physical Review Letters and has presented at conferences such as IEEE CDC 2018 and SIAM Dynamical Systems 2019. His interests include numerical optimization, software development, and automating anything.
October 4, 2019
Developing physically‐based micromechanical computational models to understand materials variability instructural applications
Hojun Lim, Department of Computational Materials and Data Science, Sandia National Laboratories
When: Friday, October 4, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Understanding mechanical behavior of polycrystalline metals using computational models requires physically-based, multi-scale materials models and quantitative validation with experiments. In addition, accurate representations of microstructures are critical in investigating process-structure-property (PSP) linkages and materials variability in performance. In this work, a meso-scale micromechanical model informed from atomistic simulations is used to predict plastic deformations of single-, multi- and poly-crystalline metals. Crystal plasticity-finite element (CP-FE) model is parameterized from molecular dynamics (MD) simulations and single crystal experiments, and used to investigate the effects of microstructural variability in local and global stress-strain responses. Heterogeneous deformations of BCC metals are quantitatively compared with various experiments. In addition, the results are used to parameterize continuum models of BCC metals and predict dynamic behaviors under extreme conditions. This framework provides an efficient and direct link from the fundamental dislocation physics to the continuum-scale plastic deformation of polycrystalline metals.
Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
About the Speaker
Dr. Hojun Lim is a Principal Member of Technical Staff at Sandia National Laboratories. He works in the field of Computational Materials Science, including multi-scale materials modeling, crystal plasticity, finite element method, and constitutive modeling, as well as mechanical properties and deformation theory of metals. He has a Ph.D. in Materials Science from The Ohio State University.
September 20, 2019
Controller Development for Cross-flow Hydrokinetic Turbines
Dr. Dominic Forbush, Sandia National Laboratories, Albuquerque, NM
When: Friday, September 20, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Cross-flow turbines, in which the axis of rotation is perpendicular to the direction of inflow, have advantages over axial-flow turbines in aquatic settings, but are not as well understood. The applicability of axial-flow controls research to cross-flow systems is limited because cross-flow turbines have unique dynamics and fewer means of control actuation. The objective is to develop and evaluate control algorithms that are broadly applicable to cross-flow turbines, specifically. Three power maximizing control laws were considered in simulation, experiment, and at field scale. While the laboratory and field-scale turbines were not directly geometrically or hydrodynamically scaled, they were morphologically similar (i.e., both were four-bladed helical turbines). Simulation was found to predict control behavior in both a time-resolved and statistical sense, and trends in controller performance were also observed at field-scale. The best-performing power maximizing controller is incorporated with a nonlinear rated power-tracking over-speed controller and a strategy to transition between the objectives proposed. The combined control law was evaluated in laboratory experiment on two cross-flow turbines, a four-bladed helical device and a turbine with two straight blades. For both turbines in all laboratory cases, constant power set points show <3% mean absolute percentage error. Error is shown to be the result of controller delay and unmitigated intra-cycle variations, particularly in the case of the straight bladed turbine. In developing and evaluating controllers, an emphasis is placed on simplicity: required sensing, the complexity of any necessary turbine characterization, and actuation requirements are all minimized to ensure that proposed controllers are broadly implementable on extant turbines. The potential costs and benefits of added system complexity can then be considered against this benchmark on a device-specific basis.
About the Speaker
Dominic is a post-doctoral researcher in the water power program at Sandia National Laboratories. His work focuses on broadly-applicable controller development approaches for wave energy devices including robust system identification techniques, power take-off design, and extreme event survival strategies. He is also involved in the development and support of the Wave Energy Convertor Simulator (WEC-Sim), an open-source block-library and support scripts that allows versatile time-domain modeling of complex wave energy devices in MATLAB/Simulink. Dominic received his Ph. D from the University of Washington (Seattle) in December 2018, and his dissertation work in the Marine Renewable Energy Laboratory focused on controller development for cross-flow hydrokinetic turbines, most notably involving a grid-connected deployment of the Ocean Renewable Power Company RivGen® device in a remote Alaskan village.
September 13, 2019
Adventures in Jet Aircraft Development
Bob Carlton, Desert Aerospace, LLC, Vertigo Airshows, Moriarty, NM
When: Friday, September 13, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Jet aircraft designer and professional airshow pilot, Bob Carlton, will take you on an adventure in the development of light jet aircraft. The presentation features technical discussion, photos, amazing videos and humorous stories from a lifetime of flying, and over a decade of designing, building and flying a series of unique jet aircraft. Bob also looks forward the future of aviation and aircraft we wouldn’t have even dreamed of a few years ago.
About the Speaker
Bob Carlton has worked in the areas of aerospace manufacturing and mechanical design for more than 35 years, including 28 years at Sandia National Laboratories. Bob has worked on numerous space-based guidance systems and aircraft-based stabilized gimbal systems. Bob was lead mechanical designer on several space and aircraft systems, including the General Atomics Lynx radar system.
September 6, 2019
In Situ Fracture Testing at the Nanoscales – Enabling Extreme Materials Design and Novel Functionalities for Next Generation Biomedical Stretchable/Wearable Devices
Prof. A. S. Budiman, Xtreme Materials Laboratory, Singapore University of Technology & Design (SUTD)
When: Friday, September 6, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Plastic deformation mechanisms in metal-metal nanolayer composites (nanolaminates) have been studied extensively during the last decade. Fracture mechanisms however have been less understood. It has been observed that, for the case of metal-metal nanolaminates with a semicoherent interface, such as Cu/Nb, low interface shear strength increases the interface barrier to dislocation crossing, which improves nanolaminate plasticity. In this study, we use Cu(63nm)/Nb(63nm) accumulative roll-bonded nanolaminates, which have a large anisotropy of the interface shear strength between rolling and transverse directions (RD and TD, respectively), to study the effect of interface shear strength on the failure in metal-metal nanolaminates with a semicoherent interface during in situ clamped beam bending. Further, finite element analysis is used to understand the observed behavior. The results show a substantial difference between the fracture behaviors along the RD and TD owing to differences in the interface shear strength and grain size. For the RD beams, the slip bands originate from the Nb layers at the notch/crack tip followed by crack propagation along these bands. For the TD beams, the crack propagation is inhibited by interface shear. We suggest that shear bands form subsequently through the beam and lead to the final beam failure. However, under the assumption of the presence of the grain boundaries near the stress concentration zone, the interface shear in the TD beams could be inhibited. In this case, the crack growth can be attributed to the formation of microcracks at grain boundaries beside the main crack. Comparison with similar Cu/Nb nanolayers produced via PVD (Physical Vapor Deposition) will be provided and mechanisms associated with plasticity and fracture will be discussed. Such advances in in situ experimentation techniques have indeed led to both extreme materials design (with enhanced fracture properties) as well as novel functionalities, such as for emergent metallic stretchable conductor technologies for next generation flexible/wearable biomedical/healthcare devices.
About the Speaker
Arief Suriadi Budiman received his B.S. in mechanical engineering from Institute of Technology, Bandung (ITB), Indonesia, his M.EngSc in materials engineering from Monash Univ., Australia and his Ph.D. in Materials Science and Engineering from Stanford University. He used to work as a post-doc at Los Alamos National Laboratory, and is currently an Assistant Professor at Singapore University of Technology and Design. Dr. Budiman has authored over 80 refereed scientific papers, conference articles, books, book chapters and patents, and is a recipient of Materials Research Society (MRS) Graduate Silver Award, MRS Best Paper Award, and Los Alamos National Laboratory Director's Research Fellowship. He is currently leading a dynamic international group researching nanomaterials and nanomechanics with applications in the next-generation solar photovoltaics, energy storage systems, and flexible biomedical devices.
April 19, 2019
Toward Additively Manufactured Continuous Carbon Fiber Reinforced Thermoplastic Composites for High Value, Low Volume Production Applications
Nekoda van de Werken , Mechanical Engineering, The University of New Mexico
When: Friday, April 19, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Carbon fiber reinforced polymer composites (CFRPs) are exceptionally strong, stiff, and lightweight materials, though conventional composite manufacturing methods have many limitations. The anisotropic nature of fiber reinforced composites necessitates larger thicknesses for complex stress states, and tooling is required which is often expensive and limits geometric complexity. Recently, continuous fiber composites have begun to enter the space of polymer additive manufacturing (AM), which presents a suite of novel opportunities and challenges. Most notably, fused filament fabrication (FFF) of thermoplastic CFRPs allows for curved fiber paths that can be placed and oriented within a part to maximize performance. In the case of semicrystalline thermoplastics, however, the rapid melting and quenching constraint imposed by AM dictates the crystalline morphology of the polymer, which may need to be post-processed to optimize part properties. The primary focus of this study is to develop an appropriate modelling and design framework that maximizes the advantages of additive manufacturing for continuous fiber composites, and to investigate the process-structure-property relationships that relate to FFF of high-temperature semi-crystalline polymer composites. The crystalline morphology, fiber-matrix interfacial properties, and composite part properties as they related to the print thermal history and post-process annealing are currently under investigation.
About the Speaker
Nekoda van de Werken received his BSME and MSME from the University of New Mexico in 2014 and 2017, respectively. He is currently working toward his PhD in mechanical engineering at UNM with an expected graduation in Fall of 2019. His research interests are in the fields of polymer matrix composites, composite mechanics, nanomaterials and characterization. Work from his master’s and PhD research has been published in high impact composites journals (Composites Part A and Composites Part B), and he was awarded the New Mexico Space Grant Consortium Fellowship in 2017. He has performed research in collaboration with Los Alamos National Laboratories, Sandia National Laboratories, and the Air Force Research Laboratories during his time as a graduate student.
April 12, 2019
Hyperbolic Method for Diffusion/Viscous Terms
Dr. Hiroaki Nishikawa, National Institute of Aerospace, Hampton, Virginia
When: Friday, April 12, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
This talk will discuss the idea of hyperbolizing diffusion/viscous terms for constructing superior numerical algorithms for diffusion and the Navier-Stokes equations. Hyperbolization allows us not only to discretize second-order elliptic/parabolic equations by methods developed for hyperbolic systems (e.g., upwind schemes), but also to generate schemes with superior features: convergence acceleration, high-order/quality derivatives on irregular grids, suitable for fully adaptive unstructured grid simulations. Moreover, it is demonstrated that hyperbolization is useful also for deriving conventional diffusion/viscous schemes as well as constructing algorithms for computing gradients. This talk is intended to provide an overview of the development of the hyperbolic method, a unique and simple idea for generating useful numerical algorithms.
About the Speaker
Dr. Nishikawa is an Associate Research Fellow at National Institute of Aerospace. He earned Ph.D. in Aerospace Engineering and Scientific Computing at the University of Michigan in 2001. He then worked as a postdoctoral fellow at the University of Michigan and joined National Institute of Aerospace in 2007. His area of expertise is the algorithm development for CFD, focusing on the hyperbolic Navier-Stokes method and related methods for unstructured-grid simulations. He is the author of a useful book on CFD: "I do like CFD, VOL.1" (cfdbooks.com).
April 5, 2019
The Near Earth Asteroid (NEA) Scout Cubesat — Attitude Determination and Control System
Brandon Stiltner, NASA Marshall Space Flight Center, Huntsville, Alabama
When: Friday, April 5, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
NEA Scout is a 6U cubesat with an 86 square-meter solar sail. NEA Scout will launch on Space Launch System (SLS) Exploration Mission 1 (EM-1). The spacecraft will rendezvous with an asteroid after a two-year journey, and will take high resolution images of the asteroid’s surface. The attitude control system consists of three major actuating subsystems: a Reaction Wheel (RW) control system, a cold-gas Reaction Control System (RCS), and an Active Mass Translator (AMT) system. The three subsystems allow for a wide range of spacecraft attitude control capabilities, needed for the different phases of NEA-Scouts mission. NEA Scout employs a solar sail for long-duration propulsion. Solar sails are large, flexible structures that typically have low bending-mode frequencies, so sail flex-avoidance is key for the ADCS. In this lecture, I’ll give an overview of the NEA Scout spacecraft, and then focus on the design of its ADCS.
About the Speaker
Brandon Stiltner is an Aerospace Engineer with over 10 years of experience from various domains of industry. He is currently employed with Jacobs Technology working as a contractor at NASA’s Marshall Space Flight Center in Huntsville, AL. Brandon’s current role is a GN&C engineer working on the development of the Space Launch System (SLS) – NASA’s new rocket that will return men to the moon. In that role, he is a member of the Liftoff and Separation Dynamics team and analyzes various events including booster separation and payload jettisons. Prior to this role, Brandon was a control system design engineer for a Cubesat project called Near Earth Asteroid (NEA) Scout. NEA Scout is a 6U Cubesat that will use a solar sail for propulsion. Its mission is to rendezvous with a near Earth asteroid, collecting high resolution images of the asteroid’s surface while also allowing scientists to better classify its orbit. Prior to NASA, Brandon was a Missile Trajectory Analyst for the Missile Defense Agency (MDA). Prior to joining the space sector of industry, Brandon was an Unmanned Aircraft Design engineer where he designed, built, and flight tested several small UAVs. Brandon holds B.S. and M.S. degrees in Aerospace Engineering from Virginia Tech and is a Certified Modeling and Simulation Professional Engineer.
March 29, 2019
Direct numerical simulations of incompressible spatially developing turbulent mixing layers
Juan D. Colmenares F., PhD Candidate, UNM Mechanical Engineering
When: Friday, March 29, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Turbulent mixing layers are a canonical free shear flow in which two parallel fluid streams of different velocities mix at their interface. Understanding spatial development of a turbulent mixing layer is essential for various engineering applications. However, multiple factors affect physics of this flow, making it difficult to reproduce results in experiments and simulations. The current study investigates sensitivity of direct numerical simulation (DNS) of such a flow to computational parameters. In particular, effects of the computational domain dimensions, grid refinement, thickness of the splitter plate, and the laminar boundary layer characteristics at the splitter plate trailing edge are considered. Flow conditions used in DNS are close to those from the experiments by Bell & Mehta (1990), where untripped boundary layers co-flowing on both sides of a splitter plate mix downstream of the plate. No artificial perturbations are used in simulations to trigger the flow transition to turbulence. DNS are conducted using the spectral-element method implemented in the open-source code Nek5000. Mean flow statistics obtained from DNS will be used for validation of high-order Reynolds-Averaged Navier-Stokes (RANS) closure models.
About the Speaker
Juan D. Colmenares F. is a PhD Candidate in the Department of Mechanical Engineering at the University of New Mexico, doing research in computational fluid dynamics (CFD). His dissertation work is focused on modeling a turbulent mixing layer using direct numerical simulations (DNS), contributing towards validation of high-order Reynolds-Averaged Navier-Stokes (RANS) closure models. This work has a potential impact on developing high-fidelity turbulence models, thus, benefitting aeronautics, aerospace, automotive and energy industries. He obtained his Bachelor’s and Master’s degree in Mechanical Engineering at the University of Los Andes (Colombia), where he developed an open-source code for aerodynamic analysis of lifting surface using the unsteady vortex-lattice method, which is currently being used in different projects.
March 22, 2019
The transition to turbulence in oscillating, Boussinesq flows near adiabatic, sloping boundaries in the abyssal ocean
Bryan Kaiser, PhD Candidate, Massachusetts Institute of Technology – Woods Hole Oceanographic Institution
When: Friday, March 22, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
About the Speaker
Bryan Kaiser is a PhD candidate in Physical Oceanography in the Massachusetts Institute of Technology - Woods Hole Oceanographic Institution (MIT-WHOI) Joint Program. His thesis work explores the role of abyssal turbulence in the upwelling branch of the global overturning circulation of the ocean, through stability analyses, direct numerical simulations, in-situ observations, and machine learning. He holds MSME (2014) and BSME (2013) degrees from UNM, and prior to his engineering education he worked at 516 ARTS in downtown Albuquerque. Next year he will be a postdoctoral researcher at Los Alamos National Laboratory, where he will research baroclinic instabilities in directdrive inertial confinement fusion and develop machine learning techniques for hydrodynamic stability estimation.
March 8, 2019
Electrical Energy Infrastructure of the Future
Amir Sajadi, Senior Engineer, Public Services Commission of Wisconsin
When: Friday, March 8, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
The future energy infrastructure will be composed of hundreds of thousands of controllable and uncontrollable components that function in numerous ways. It also will involve high proliferation of renewable energy resources, such as solar, wind, and energy storage systems that could be integrated into the transmission and distribution networks. This complex system will manifest a sophisticated dynamic behavior and broad limitations in its control and operation. Accordingly, implementing high degrees of visibility and controllability, further integration of communication and advanced control infrastructures, and engagement of consumers are pivotal. This is to pave the path for advancements in energy management systems and to ensure a reliable, stable and secure power delivery. Considering the above-mentioned issues, the central gravity of my research plan is to focus on planning, management, stability, dynamics, and control problems associated future energy infrastructure and electric grid modernization.
In this talk, I will present the findings from two projects. The first project focuses on transmission system planning for the integration of large renewable power plants. My work, with the GE, NREL, FirstEnergy, and PJM, produced the guideline for the US Department of Energy on transmission systems planning using the US Eastern Interconnection. This guideline includes a series of techniques to determine operational impacts of offshore wind generation on steady-state and dynamic stability of large-scale power systems. The second project relates to real-time operation and control of future power systems. A crucial operating constraint for power systems is transient system stability. My work developed a computational framework for identification of multidimensional transient stability boundaries as well as critical operating conditions in a high-dimensional space for operation and stability.
About the Speaker
Amir Sajadi is a Senior Engineer at the Public Services Commission of Wisconsin where he oversees the planning and operation of the regional electric transmission services and wholesale energy market. He is also an Adjunct Assistant Professor of Systems and Control Engineering at the Case Western Reserve University in Cleveland, Ohio, and an Honorary Fellow in the Power Systems Engineering Research Center (PSERC) at the University of Wisconsin-Madison, Wisconsin. The areas of his expertise include modelling, operation, stability, and control of electric energy and power systems including the integration of renewable energy sources, storage systems, and electric vehicles. Amir attended various international universities under an international consortium including: Warsaw University of Technology in Poland, RWTH Aachen University in Germany, Telecom ParisTech in France, and University of Waterloo in Canada. He graduated with a M.Sc. in Electrical Engineering in 2012 from the Warsaw University of Technology. Subsequently, he earned a Ph.D. in Systems and Control Engineering in 2016 from the Case Western Reserve University in Cleveland, Ohio and then, between 2016 and 2018, he conducted his Postdoctoral research in Future Power Systems at the University of Manchester in the United Kingdom. Amir has authored approximately 40 international scientific and industrial publications and has spoken at leading power system conferences around the world. Currently, he is serving as the lead guest editor of the special issue of International Journal of Electrical Power & Energy Systems on Recent Advancements in Electric Power System Development Planning with High-Penetration of Renewable Energy Resources and Dynamic Loads.
March 1, 2019
Title TBA
Kenneth M. Armijo, Senior Member of the Technical Staff, Concentrating Solar Energy Technologies Department, Sandia National Laboratories
When: Friday, March 1, 2019, 3:30 - 4:30 PM
Where: MECH 218
February 22, 2019
Model Fidelity Studies for Rapid Trajectory Optimization
Lisa Hood, Member of Technical Staff, Navigation, Guidance, & Control II Division, Sandia National Laboratories
When: Friday, February 22, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
The generation of optimal trajectories for test flights of hypersonic vehicles with highly non-linear dynamics and complicated physical and path constraints is often time consuming and sometimes intractable using high-fidelity, software-in-the-loop vehicle models. Practical use of hypersonic vehicles requires the ability to rapidly generate a feasible and robust optimal trajectory. We propose a solution that involves interaction between an optimizer using a low fidelity 3-DOF vehicle model and feedback from vehicle simulations of varying fidelities, with the goal of rapidly converging to a solution trajectory for a hypersonic vehicle mission. Further computational efficiency is sought using aerodynamic surrogate models in place of aerodynamic coefficient look-up tables. We address the need for rapidly converging optimization by considering how to choose the fidelity of the model used for optimization so that the resulting guidance solution is robust and feasible, but the computation time to generate it is minimized.
About the Speaker
Lisa Gammon Hood is a member of technical staff at Sandia National Laboratories, working in the Navigation, Guidance, & Control II division. Lisa received a Master's degree in aerospace engineering from Georgia Tech in 2018 and a Bachelor's degree in aerospace engineering from Georgia Tech in 2003. Lisa's current work focuses on trajectory optimization and conceptual design for hypersonic vehicles.
February 8, 2019
Toward a Distributed and Automated Control Framework in Power Systems
Dr. Ali Bidram, Assistant Professor, Electrical and Computer Engineering, The University of New Mexico
When: Friday, February 8, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Abstract Conventional electric power systems are facing continuous and rapid changes to alleviate environmental concerns, address governmental incentives, and respond to the consumer demands. The notion of the smart grid has emerged to introduce an intelligent electric network. Improved reliability and sustainability are among desired characteristics of smart grid affecting the distribution level. These attributes are mainly realized through microgrids which facilitate the effective integration of distributed generators (DG). Microgrids can operate in both grid-connected and islanded operating modes. Proper control of microgrid is a prerequisite for stable and economically efficient operation. Microgrid technical challenges are mainly realized through the hierarchical control structure, including primary, secondary, and tertiary control levels. Primary control level is locally implemented at each DG, while the secondary and tertiary control levels are conventionally implemented through a centralized control structure. The centralized structure requires a central controller which increases the reliability concerns by posing the single point of failure. Alternatively, the distributed control structure using the distributed cooperative control of multi-agent systems can be exploited to increase the secondary control reliability. The secondary control objectives are microgrid voltage and frequency, and DG active and reactive powers. Fully distributed control protocols can be implemented through distributed communication networks. Since the DG dynamics are nonlinear and non-identical, input-output feedback linearization can be used to transform the nonlinear dynamics of DGs to linear dynamics. The transformed dynamics of DGs are then being used in the design of distributed control protocols. In the distributed control structure, each DG only requires its own information and the information of its neighbors on the communication network. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability.
About the Speaker
Dr. Michael Bilka received his PhD from the von Karman Institute and Vrije Universiteit Brussels in Belgium. From there he moved to the University of Notre Dame as a postdoctoral researcher. He then stayed on as Senior Scientist in the Notre Dame Turbomachinery laboratory and held a concurrent appointment as a Research Assistant Professor in the Department of Aerospace and Mechanical Engineering. In 2017 he left Notre Dame to join Ball Aerospace in Albuquerque where he currently works as a Research Engineer in the Effects, Research and Analysis group. His research interests include high speed and high enthalpy flows, turbomachinery flows, flow induced sound and vibration and unsteady instrumentation development.
February 1, 2019
Unsteady measurement techniques with application to turbomachinery flows and sound generation
Dr. Michael Bilka, Research Engineer, Effects, Research and Analysis, Group Ball Aerospace
When: Friday, February 1, 2019, 3:30 - 4:30 PM
Where: MECH 218
Abstract
Continued development of advanced simulation and design tools required increased fidelity measurements for verification and validation and detailed physical understanding. Many fluid flow problems of technological interest involve complex geometries and unsteady, turbulent flows. The development and validation of unsteady measurement techniques is needed to help further develop design and computational tools to advance quieter and more efficient technologies. In this talk the development of unsteady pressure and temperature instruments will be discussed. These techniques will be applied to turbomachinery and sound generating flows to help elucidate important flow f eatures that can lead to improved component efficiency and decreased sound generation.
About the Speaker
Dr. Bidram is currently an Assistant Professor in the Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA. He has received his B.Sc. and M.Sc. from Isfahan University of Technology, Iran, in 2008 and 2010, and Ph.D. from the University of Texas at Arlington, USA, in 2014. Before joining the University of New Mexico, he worked with Quanta Technology, LLC, and was involved in a wide range of projects in the electric power industry. He is an Associate Editor for the IEEE Transactions on Industry Applications. His areas of expertise lie within control and coordination of energy assets in power electronics-intensive energy distribution grids. Such research efforts have culminated in a book, several journal papers in top publication venues and articles in peer-reviewed conference proceedings, and technical reports. He has received the University of Texas at Arlington N. M. Stelmakh outstanding student research award, Quanta Technology Shooting Start award, and cover article of December 2014 in IEEE Control Systems.