BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.3.1//EN TZID:America/Los_Angeles X-WR-TIMEZONE:America/Los_Angeles BEGIN:VEVENT UID:263@escience.washington.edu DTSTART;TZID=America/Los_Angeles:20250402T163000 DTEND;TZID=America/Los_Angeles:20250402T172000 DTSTAMP:20250331T205613Z URL:https://escience.washington.edu/events/uw-data-science-seminar-kevin-m anohar/ SUMMARY:UW Data Science Seminar: Kevin Manohar DESCRIPTION:Please join us for a UW Data Science Seminar featuring UW Aeron autics and Astronautics PhD student Kevin Manohar on Wednesday\, April 2n d from 4:30 to 5:20 p.m. PT.\n\n\nThe seminar will be held in Electrical a nd Computer Engineering Building 125 – Campus Map.\n"Experimental Data Assimilation of Multi-Scale Turbulent Separated Flows"\n\nAbstract: Turbul ent separated flows are among the most challenging and industrially releva nt\nphenomena in fluid dynamics\, arising in scenarios like aircraft takeo ff\, 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 airc raft 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 physic s-informed hybrid deep learning models. While such datasets are relatively common\, they typically capture only partial snapshots of the full dynami cs (e.g.\, 2D measurements of 3D flows) and are often noisy and incomplete . Unlike many existing models tested only on clean\, laminar\, or simulate d data\, our work tackles measurement data at high-Reynolds-number regimes with large scale-separation. We combine multimodal measurements—high-re solution but low-rate velocity flow-fields from particle image velocimetry (PIV)\, and high-rate but low-resolution wall-pressure signals—to recon struct more complete representations of the flow. Key challenges include t emporal super- resolution\, inference of unmeasured variables\, and volume tric reconstruction from independently acquired PIV planes. I will highlig ht these challenges\, the unique datasets to be collected within the Boein g Common Research Model ecosystem\, our modeling framework\, and a case st udy on turbulent flow over a speed-bump geometry. This research enables ne w possibilities for multimodal sensor-based estimation frameworks\, with p romising implications for flow control\, monitoring\, and design in comple x aerodynamic systems.\n\n Biography: Kevin is a second-year PhD studen t in the Department of Aeronautics and Astronautics at the University of W ashington. His research lies at the intersection of experimental fluid tur bulence and data assimilation\, with a focus on leveraging physics-informe d machine learning to extract insights from sparse\, multimodal flow data. He is an NSERC (Natural Sciences and Engineering Research Council of Cana da) Doctoral Scholar and was a 2023–24 Herbold Data Science Fellow. Kevi n holds a BSc and MSc in Mechanical Engineering from the University of Cal gary and has completed research fellowships at institutions including the Paul Scherrer Institute in Switzerland and CentraleSupélec in France\, wh ere he worked on scientific machine learning applications in turbulent flo w physics. \nThe 2024-2025 seminars will be held in person\, and are free and open to the public. ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa ds/2025/03/headshot_2-scaled.jpg END:VEVENT BEGIN:VTIMEZONE TZID:America/Los_Angeles X-LIC-LOCATION:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR