ABSTRACT: The use of numerical simulations for external aerodynamic applications has been a key tool for aircraft design in the modern aerospace industry. However, predictions from the state-of-the-art solvers are often unable to comply with the stringent accuracy requirements demanded by the industry. In this talk, I discuss outstanding challenges in numerical simulations of real-world aerodynamic applications from UAVs to hypersonic vehicles. I also discuss how advancements in artificial intelligence can aid the design of novel numerical models. In that vein, I show an example of a 'self-critical' machine learning model to predict the lift coefficient of a commercial aircraft using wall-modeled large-eddy simulation.
BIO: Adrian Lozano-Duran is the Draper Assistant Professor of Aeronautics and Astronautics at MIT AeroAstro. He received his Ph.D. in Aerospace Engineering from the Technical University of Madrid in 2015. From 2016 to 2020, he was a Postdoctoral Research Fellow at the Center for Turbulence Research at Stanford University. His research is focused on Computational Fluid Mechanics and physics of Wall Turbulence. His work includes turbulence theory & models by Artificial Intelligence and large-eddy simulation, high-speed flows, and multiphase flows, among others.