Please join us for a UW Data Science Seminar featuring UW Aeronautics and Astronautics PhD student Kevin Manohar on Wednesday, April 2nd from 4:30 to 5:20 p.m. PT.
The seminar will be held in Electrical and Computer Engineering Building 125 – Campus Map.
“Experimental Data Assimilation of Multi-Scale Turbulent Separated Flows”
Abstract: Turbulent separated flows are among the most challenging and industrially relevant
phenomena in fluid dynamics, arising in scenarios like aircraft takeoff, landing, and complex maneuvers. These flows are strongly nonlinear, multiscale, unsteady, and three-dimensional—making them difficult to simulate or predict, and often the most limiting and costly phase in aircraft certification. In this talk, I will present my research that aims to utilize experimental flow data to reconstruct unmeasured flow information—such as missing variables, frequencies, or dimensions— using physics-informed hybrid deep learning models. While such datasets are relatively common, they typically capture only partial snapshots of the full dynamics (e.g., 2D measurements of 3D flows) and are often noisy and incomplete. Unlike many existing models tested only on clean, laminar, or simulated data, our work tackles measurement data at high-Reynolds-number regimes with large scale-separation. We combine multimodal measurements—high-resolution but low-rate velocity flow-fields from particle image velocimetry (PIV), and high-rate but low-resolution wall-pressure signals—to reconstruct more complete representations of the flow. Key challenges include temporal super- resolution, inference of unmeasured variables, and volumetric reconstruction from independently acquired PIV planes. I will highlight these challenges, the unique datasets to be collected within the Boeing Common Research Model ecosystem, our modeling framework, and a case study on turbulent flow over a speed-bump geometry. This research enables new possibilities for multimodal sensor-based estimation frameworks, with promising implications for flow control, monitoring, and design in complex aerodynamic systems.
Biography: Kevin is a second-year PhD student in the Department of Aeronautics and Astronautics at the University of Washington. His research lies at the intersection of experimental fluid turbulence and data assimilation, with a focus on leveraging physics-informed machine learning to extract insights from sparse, multimodal flow data. He is an NSERC (Natural Sciences and Engineering Research Council of Canada) Doctoral Scholar and was a 2023–24 Herbold Data Science Fellow. Kevin holds a BSc and MSc in Mechanical Engineering from the University of Calgary and has completed research fellowships at institutions including the Paul Scherrer Institute in Switzerland and CentraleSupélec in France, where he worked on scientific machine learning applications in turbulent flow physics.
The 2024-2025 seminars will be held in person, and are free and open to the public.