School of CSE Seminar Series: Adrian Lozano-Duran

Speaker: MIT Assistant Professor Adrian Lozano-Duran
Date and Time: April 19, 2:00-3:00 p.m.
Location: Coda 114
Host: Spencer Bryngelson

Title: Building-block Flow model for Computational Fluids

Abstract: The predictive capabilities of computational fluid dynamics (CFD), critical for aerodynamic design, hinge on the development of accurate closure models. However, no practical model has emerged as universally applicable across the broad range of flow regimes of interest to the industry. We introduce a closure model for CFD, referred to as the Building-block Flow Model (BFM). The foundation of the model rests on the premise that a finite collection of simple flows encapsulates the essential physics necessary to predict more complex scenarios. The BFM is implemented using artificial neural networks and introduces five unique advancements within the framework of large-eddy simulation: (1) It is designed to predict multiple flow regimes (laminar flow, wall turbulence under zero, favorable, and adverse mean-pressure gradients, separation, statistically unsteady turbulence, and wall roughness effects); (2) It leverages information-theoretic dimensional analysis to select the most relevant non-dimensional input/output variables; (3) It ensures consistency with numerical schemes and gridding strategy by accounting for numerical errors; (4) It is directly applicable to arbitrary complex geometries; (5) It can be scaled up to model additional flow physics in the future if needed (e.g., shockwaves and laminar-to-turbulent transition). The BFM is utilized to predict key quantities of interest in a wide range of cases, such as turbine blades with roughness, speed bumps, and aircraft in landing configuration. In all cases, the BFM demonstrates similar or superior capabilities in terms of accuracy and computational efficiency compared to previous state-of-the-art closure models. We also discuss ongoing efforts to extend the model to supersonic and hypersonic flows with applications to Entry, Descent and Landing (EDL) vehicles. 

Bio: Adrian Lozano-Duran is the Boeing Assistant Professor at MIT AeroAstro. He is also a faculty at the MIT Center for Computational Science and Engineering. 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 turbulence. His work includes turbulence theory using graph theory and information theory, and reduced-order modeling for computational fluids by artificial intelligence.