Seminar: Numerical Methods for Partial Differential Equations

Speaker:  Adrián Lozano-Durán  (MIT)

Title:  Machine-learning building-block model for computational fluid dynamics

Abstract:

One of the primary factors hindering the adoption of transformative low-emissions aircraft designs is the time-consuming (taking years) and costly (costing billions of dollars) experimental campaigns required during the design cycle. Computational fluid dynamics (CFD) might accelerate the process and alleviate the cost. However, current turbulence models do not meet the stringent accuracy requirements demanded by the industry. Here, we have devised a new closure model for CFD to bridge the gap between our current predictive capabilities and those required by the aerospace industry. This model, referred to as the building-block-flow model, conceives the flow as a collection of simple units that contain the essential flow physics necessary to predict complex flows. The approach is implemented using two artificial neural networks: a classifier that identifies the contribution of each building block in the flow, and a predictor that estimates the effect of missing scales through a combination of the building-block units. The training data are directly obtained from CFD with exact modeling for mean quantities to ensure consistency with the numerical discretization. The model's output is accompanied by confidence in the prediction, which is used for uncertainty quantification. The model is validated in realistic aircraft configurations.

Short 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.