AE Presents: Adrian Lozano-Duran

Thu Jan 30 2020 03:30 PM to 04:30 PM
MK 317
"New Opportunities in Fluid Dynamics Research: from Theory to Reduced-Order Modeling of Real-World Applications"

The Daniel Guggenheim School of Aerospace Engineering

invites you to attend the talk 

 

"New Opportunities in Fluid Dynamics Research: from Theory to Reduced-Order Modeling of Real-World Applications"

 

by
 

Dr. Adrian Lozano-Duran 

Postdoctoral Research Fellow | Stanford University

 

Thursday, January 30
3:30 - 4:30 pm
MK 317

About the Talk:
Even after more than a century, turbulent flows research is deemed to be in its infancy by the most critical researchers in the field. And they might be right. Although we possess a crude practical understanding of turbulence, we still lack a theory capable of providing the accurate predictions demanded by the industry at an affordable computational cost. In the present talk, I discuss new and future advancements in the physical understanding and modeling of turbulent flows. These include the use of Graph and Information Theory as a novel framework for quantifying causality in fluid dynamics, and Machine Learning techniques for discovering new theory and models in analytical form free from the intricacies of traditional neural networks. I further discuss how flow control and modeling strategies can take advantage of these new advancements. As an example, I show improvements in the aerodynamic efficiency of a full aircraft in stall at realistic Reynolds numbers via reduced-order modeling of active flow control.

About the Speaker:
Dr. Adrian Lozano-Duran is a Postdoctoral Research Fellow at the Center for Turbulence Research at Stanford University hosted by Prof. Moin. He received his PhD in Aerospace Engineering from the Technical University of Madrid in 2015 at the Fluid Mechanics Lab. headed by Prof. Jiménez.  His main research is focused on Computational Fluid Mechanics with emphasis on wall-bounded turbulence. His work covers a wide range of topics such as turbulence theory and modeling by machine learning, large-eddy simulation for external aerodynamics, high-speed flows, geophysical flow, and multiphase flows, among others.

Location

MK 317