Fernando Zigunov, Ph.D.Assistant Professor at Syracuse University, NY.
Friday, April 5, 2024, 3:00 - 4:00 PM
In Person JH-109
Title: Towards digital flow control: Understanding and controlling high-speed flows with machine learning techniques
ABSTRACT: Turbulent, high-speed flows present formidable control challenges to engineers and researchers in the aerospace and mechanical engineering industries. On one hand, we understand and are capable of modeling and predicting turbulence behavior to a great degree of accuracy through the advancements of computational fluid dynamics. On the other hand, attempting to control turbulence with linear or nonlinear control techniques may remain intractable for the foreseeable future.
In this seminar, we will discuss the recent advancements in using machine learning techniques applied to active flow control, and how it may offer promising and practical solutions to industry problems such as drag reduction, noise reduction, among others. We will also discuss the current state-of-the-art techniques used in aerospace research to measure, analyze and understand high-speed flows. These techniques are invaluable tools that will enable the next generation of aerospace engineers to develop novel, more efficient and effective aircraft.
BIO: Dr. Fernando Zigunov is an Assistant Professor at Syracuse University, NY. He obtained his PhD in Mechanical Engineering in 2020 at Florida State University, and was previously a Postdoctoral Researcher at Los Alamos National Laboratory. His research interests include volumetric flow diagnostics applied to supersonic aerodynamics, machine learning applied to flow control, and data assimilation for experimental fluid mechanics.
