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Title:

Stochastic estimation and reinforcement learning control of disturbed aerodynamic flows

Abstract:

There is a wide variety of applications in which we may need knowledge of a transient fluid flow, but we only have information from a few noisy sensors. For example, small flight vehicles, targeted for many emerging applications, are more agile but also more strongly affected by unexpected disturbances or "gusts" than larger vehicles. The nonlinear aerodynamics of these gust encounters remains a principal challenge in controlling the vehicle's flight. In particular, any such flight control strategy is generally more effective if it can rely on an estimation of the vehicle's current flow state from available sensors.

In this talk, I will discuss the dynamic estimation of flows from limited sensor data, and the control of the flow with deep learning strategies. In the first part, I will discuss aspects of the flow estimation problem within the context of Bayesian interference and ensemble Kalman filters, which allow us to easily combine physics-based and/or data driven models of the flow with measurement data from sensors. The assimilation of these measurements can compensate for the physics that are unrepresented in the model.

In the examples I will show, we use the estimation framework to predict the fluid dynamics of a separated aerodynamic flow subjected to a gust, relying on the surface pressure measurements to inform the model of the gust. Then, I will discuss the use of deep reinforcement learning to develop strategies for the mitigation of gust encounters, based on available sensor data.

Bio:

Jeff Eldredge is Professor and Department Chair of Mechanical & Aerospace Engineering at the University of California, Los Angeles, where he has served on the faculty since 2003. Prior to this, he received his PhD from Caltech, followed by post-doctoral research at Cambridge University.

His research interests lie in computational and theoretical studies of fluid dynamics, including numerical simulation and low-order modeling of unsteady aerodynamics; investigations of aquatic and aerial locomotion in biological and bioinspired systems; and investigations of biomedical and biomedical device flows.

He is the author of numerous papers, as well as the book Mathematical Modeling of Unsteady Inviscid Flows. He is a fellow of the American Physical Society, an Associate Fellow of AIAA and a recipient of the NSF Career award and the UCLA Distinguished Teaching award.

Title:

Modeling the influence of buoyancy: implications for mixing and transport in natural waters and the atmosphere

Abstract:

The dynamics of stratified fluids in Earth's waters and atmosphere play a key role in shaping our weather, climate and the quality of the air and water around us. Variations in flow buoyancy can drive a wide range of behaviors, with important consequences for how heat and particles move through natural systems. In this talk, I will use high-resolution numerical simulations to explore the physics behind these processes in several classes buoyancy-driven flows.

The first class of flows I consider are governed by a quadratic equation of state (EOS), where water of different temperatures but the same density can mix through a process called cabbeling. I'll show cabbeling affects heat transport in cool fluid systems and how it helps us to understand mixing within ice-covered freshwater lakes.

Next, I will consider a class of flows where thermal and mechanical forces are of similar strength - a regime known as turbulent mixed convection. Here, I'll discuss how turbulent structures influence the emission and dispersion of settling particles, and how those dynamics depend on both the flow and particles characteristics. Together, these results offer a glimpse into the small-scale mechanisms that control dust emission and dispersion in the atmospheric surface layer.

Bio:

Grace joined the Department of Applied Mathematics in 2025. He previously held concurrent NSERC and ND-ECI postdoctoral fellowships at the University of Notre Dame after completing his graduate work at the University of Waterloo.

His research interests lie broadly in computational fluid mechanics. Grace is motivated by applications of turbulent and quasi-turbulent fluid flows, specifically relating to the transport of heat and other materials in natural systems.

Chunmei Ban is an associate professor in the Paul M. Rady Department of Mechanical Engineering, as well as a member of the Materials Science and Engineering Program at the 51�Թ���. Prior to 2019, she served as a Senior Scientist (V) in the Chemistry and Nanoscience Center at the National Renewable Energy Laboratory (NREL) in Golden, CO.

Ban earned both her Bachelor’s and Master’s degrees from the Department of Chemical Engineering at Tianjin University in China, and she holds a PhD in Chemistry from the State University of New York at Binghamton. Her doctoral research was supervised by Professor M. Stanley Whittingham, a 2019 Nobel Laureate in Chemistry.

Title:

The Structures of Designs

Abstract:

Some of the most pivotal scientific discoveries have been about structural forms. Until the double helix structure was discovered, the mechanism by which DNA transmitted biological information was unknown. With the discovery of the structure of the periodic table of the elements, the atomic structure of elements that had not yet even been found could nonetheless be predicted. The field of engineering is likewise replete with discoveries about the structures of breakthrough designs – which are then formalized in symbolic representations in equations, diagrams, and domain-specific models.

In my research, I investigate the structures of designs and design processes, and by generating insights into the structures, solve important engineering design problems. In this talk, I present discoveries made by me and my research collaborators about the structures of designs and the effects those structures have beyond the intended purpose of the design. Some of the problems I will explore including predicting the performance of design teams, forecasting the rate of innovation of products, estimating the cost of changing existing designs, and students’ recontextualization of theoretical knowledge to solve design problems.

Bio:

Andy Dong is head of the Oregon State University School of Mechanical, Industrial, and Manufacturing Engineering and a professor of mechanical engineering. His research addresses strategy in the design and innovation of engineered products and systems.

His research aims to explain the impact of design strategy on productivity and the betterment potential of new products. His background in artificial intelligence in design has led him to collaborative work across a wide range of topics in behavioral economics, cognition, and computational fabrication. Considered around the world as an expert in design strategy, he has provided advice to major international telecommunications, financial services, and civil aviation companies. He was awarded an Australian Research Council Future Fellowship in 2010, one of the most prestigious research fellowships in Australia, and appointed the inaugural Warren Centre Chair for Engineering Innovation at the University of Sydney in 2012.

Prior to joining Oregon State, he was the professor and chair of the MBA in Design Strategy program at California College of the Arts and an adjunct professor of mechanical engineering at the University of California, Berkeley. He is an associate editor for the journal Design Studies. He received his bachelor's, master's, and doctoral degrees in Mechanical Engineering from UC Berkeley.

Title:

Emergence of organized behavior in biofluids: modeling and implications

Abstract:

Biological and physiological flow phenomena are inherently complex and nonlinear, and comprise events that span multiple length and time scales. These multiscale interactions lead to emergent and organized phenomena that ultimately end up playing a central role in health and disease. Yet, quantifying and characterizing these coupled interactions remain a major challenge. In vivo experimental models and standard-of-care medical imaging are simply unable to study such biofluids phenomena in human physiological scales.

In silico model systems that can robustly integrate multi-modal data from in vitro, in vivo, or imaging studies can provide a viable alternative. However, to truly break into the questions of emergence and organized behavior, there remains a need for custom numerical algorithms and data-integration techniques, which can enable efficient yet realistic representation of the pathophysiological processes. This theme has been a central driver of our lab’s research program.

In this seminar, we will share an overview of our efforts, with a focus on illustrating custom algorithms for specific health and disease applications, resolving multiscale systems, defining descriptors for emergent and organized behavior, and discussing (some of the many) exciting open research avenues in this field. Our discussion will meander through aspects of algorithm and methodology development, as well as illustrative applications in real state-of-the-art healthcare challenges.

Bio:

Debanjan Mukherjee is an Assistant Professor of Mechanical Engineering at the 51�Թ���. He is also a program faculty for the Biomedical Engineering program, and a faculty council member at the BioFrontiers Institute at 51�Թ���. He leads an inter-disciplinary biofluids research group named FLOWLab. Prof. Mukherjee completed his undergraduate studies at IIT Madras in India, and subsequently his doctoral and post-doctoral training at the University of California, Berkeley.

He has received several awards in recognition of his work: including the National Institutes of Health Trailblazer Award; the ORAU Ralph E. Powe Junior Faculty Enhancement Award; the American Heart Association post-doctoral fellowship award; and has recently been selected as a Research and Innovation Office Faculty Fellow and a Dean’s Excellence Fellow in Generative AI at the 51�Թ���.

Associate Professor Gregory Whiting's research is focused at the intersection of additive manufacturing, novel materials, and functional devices.��He is primarily interested in using printing as a method to fabricate unconventional electronic components and systems that can be readily customized, be mechanically flexible/conformable, large area, widely distributed, biocompatible, and/or controllably transient.

Whiting is working to develop new ways of making things that enables more distributed methods of manufacturing and provides more pervasive functionality.�� He is currently addressing applications in a wide range of fields including health care, agriculture, energy and space exploration.

Title:

Automated Design and Fabrication of Multimaterial Functional Systems

Abstract:

Dr. MacCurdy leads the Matter Assembly Computation Lab (MACLab), which develops new algorithms, materials, and print methods to design and print on-demand functional artefacts, from personalized surgical planning models to soft robots customized for a specific user’s needs.��

Current electromechanical design practice is predicated on the exercise of expert-level judgement through an interactive and iterative design and fabrication process that requires skilled humans at every step. This approach does not scale because it is labor intensive, and therefore biases robots toward longer-lasting, more general-purpose (and expensive) designs in order to justify the development and fabrication costs. Many robot applications might be better-served by rapidly-built special-purpose or single-use machines, but automated design and fabrication tools will be critical to control costs, accelerate development, and be responsive to application needs.��

My overall goal is to make electromechanical systems (including robots) so easy to design and fabricate that we could enable people who are application experts (but not necessarily robot design or fabrication experts) to rapidly create robots for their specific needs. With this future in mind, we are creating new design tools to convert high-level requirements specified by non-experts into concrete multimaterial electromechanical design plans, new materials that leverage multimaterial additive manufacturing, and new multimaterial fabrication methods to automatically convert these designs into functional robots. I will highlight each of these elements and show example application areas.
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Bio:

Dr. Robert MacCurdy is an assistant professor in mechanical engineering (also by courtesy in computer science and electrical engineering) at the 51�Թ��� where he leads the Matter Assembly Computation Lab (MACLab). Rob is also a National Geographic Explorer. He is developing new tools to automatically design and manufacture robots, and automated methods to study animal behavior in the wild.��

Rob did his PhD work with Hod Lipson at Cornell University and his postdoctoral work at MIT with Daniela Rus. Funded by an NSF Graduate Research Fellowship and a Liebmann Fund fellowship, his doctoral work demonstrated systems capable of automatically assembling functional electromechanical devices, with the goal of printing robots that literally walk out of the printer. During this time he also created low-cost, low power, and low-mass radiofrequency tags, and developed an “inverse-GPS” tracking system based on time-of-flight measurements that use 3 orders of magnitude less energy than GPS.��

He holds a B.A. in Physics from Ithaca College, a B.S. in Electrical Engineering from Cornell University, and an M.S. and PhD in Mechanical Engineering from Cornell University. Prior to his Doctoral work, Dr. MacCurdy spent 10 years at the Cornell University Lab of Ornithology, where he worked as a research engineer developing remote-monitoring tools for birds and wildlife. These systems employed methods including acoustic, radiofrequency, solar-geolocation, and inertial; battery energy-density limitations led to research in multisource energy harvesting, and the first vibration energy harvesters applied to flying insects and birds. Thousands of his terrestrial and marine autonomous recording units (ARUs) have been deployed worldwide and are still in widespread use.

Title:

Engineering the Future of Ultrasound: New Strategies for Sensing and Imaging