Jacqueline O'Connor

Professor, Department of Mechanical Engineering
Pennsylvania State University

Use of information networks for understanding hydrodynamic instability on complex wake flows

August 27, 2026

Hydrodynamic instability drives the performance and operability of a number of aerospace systems, including aircraft, rotorcraft, and propulsion systems. Identifying the source of the instability and its dynamics in complex configurations is challenging, particularly in highly turbulent, three-dimensional environments. In this talk, we'll explore the use of information network theory for identifying the critical regions of unstable flows. We apply these methods to wake flows, both single- and multi-wake systems, to explore opportunities for better insight into complex flow behaviors.

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Dr. Jacqueline O'Connor is a professor of mechanical engineering at Penn State and directs the Reacting Flow Dynamics Laboratory. She and her students study issues related to combustor operability, alternative fuels, and high-temperature material durability and heat transfer for power and propulsion applications. She received a B.S. from MIT and an M.S. and Ph.D. from Georgia Tech. She was a post-doctoral researcher at Sandia National Laboratories before starting at Penn State in 2013. She is a fellow of the American Society of Mechanical Engineers and an associate fellow of the American Institute of Aeronautics and Astronautics.

Aditya Nair

Associate Professor, Department of Mechanical Engineering
University of Nevada, Reno

Data-driven approaches for simulation, modeling and control of unsteady fluid flows

September 3, 2026

Unsteady flows are expensive to simulate and hard to control. But their dynamics are rarely spread evenly. The physics that matters concentrates in a few regions of space and a few moments in time. This talk presents a suite of data-driven strategies built on that premise.

In space, dominant balance analysis locates where the governing physics is active. An adaptive mesh refinement framework uses this to allocate resolution where it is needed, cutting cost without sacrificing accuracy. Force and moment partitioning offers the complementary view, attributing unsteady loads to the specific vortical structures that generate them.

In time, spectral POD with triadic interaction mapping reveals how energy transfer between scales is regulated by modal amplitude. Phase-amplitude reductions identify when an oscillatory flow is most receptive to actuation. Cluster-based latent control coarse-grains the dynamics into transitions among representative states, and acts on those states directly in a learned latent space, with no model of the full system required.

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Dr. Aditya G. Nair is an Associate Professor in the Department of Mechanical Engineering at the University of Nevada, Reno. His research interests are in the areas of computational fluid dynamics, high-performance computing, data science, and control theory focused on modeling and control of unsteady fluid flows. He received his M.S. from University of Michigan in 2013 and Ph.D. from Florida State University in 2018. Dr. Nair is the recipient of the Department of Energy Early Career Award in 2022, AFOSR DEPSCoR award in 2023 and is a founding member of the NSF AI institute of Dynamic Systems.

Matthew Krull

Doctoral Candidate, Department of Mechanical Engineering
Pennsylvania State University

Experimental and computational characterization of transonic turbine relevant flow fields

September 17, 2026

Simultaneous pressure and velocity measurements of compressible flows are essential for understanding fundamental flow phenomena such as shockwave–boundary layer interactions, influence of surface cooling/film cooling, or supersonic jet noise. To date, there are few measurement techniques that can obtain both quantities in the flow simultaneously. Of particular interest is development of non-intrusive, spatially resolved techniques since probes can interfere with the flow, and spatial resolution of complex flow interactions is important to identify sources of noise. Recent developments in particle image velocimetry have shown that by measuring the velocity field and its gradients, the pressure field can be inferred with adequate boundary information and assumptions about the flow (namely, either that it is incompressible, or is adiabatic if compressible).

This presentation will focus on establishing a robust algorithm to determine pressure from velocimetry measurements for adiabatic flows, and developing a framework to acquire temperature from thermographic velocimetry measurements for nonadiabatic flows. An algorithm to extract three-dimensional pressure from tomographic particle image velocimetry data for compressible nonadiabatic flows has been completed and demonstrated. Additionally, experimental measurements of the wake behind large-scale and true-scale airfoils have been measured, showing that pressure information can be experimentally obtained from particle image velocimetry data. Large Eddy Simulation (LES) has also been utilized to capture Reynolds- and Favre-averaged quantities to evaluate their impact on the pressure reconstruction algorithm.

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Mr. Matthew Krull is a Ph.D student in Mechanical Engineering and a NASA Advanced Air Vehicles Program (AAVP) graduate fellow at Penn State University. He works in the Experimental and Computational Convection Laboratory (ExCCL) studying turbine aerodynamics and heat transfer. He received his M.S. in Mechanical Engineering at Penn State University, and his B.S. in Mechanical Engineering and a minor in Physics at Penn State Behrend.