An Efficient Data-Driven Approach for Assessment and Selection of Reynolds-Stress-Equation Closure Models

Speaker: Ali Mani, Department of Mechanical Engineering, Stanford University
May 15, 2025

Presentation

Abstract

Data-driven closure models often involve many adjustable parameters, typically by orders of magnitude more than traditional closure models. Such a large parameter space is needed to enable fitting of complex input-output dependencies regardless of the interpretability of the target closure models. A crucial challenge is therefore providing reference data that probes the sensitivities in the parameter space. As a result, most data-driven closure frameworks have access to substantially less data than needed to tune their closure models.

To address this need, we present an approach for systematic generation of reference data that can economically sample diverse regions in the parameter space. This approach is based on the Macroscopic Forcing Method and its extension for second moment closures in which volumetric forcing terms and boundary conditions are utilized to diversify the sampled flow conditions. Furthermore, we demonstrate a systematic selection of the forcing terms to generate data to probe specific subsets of closure parameters, thereby streamlining the model tuning process in a fashion tailored for the selected closure framework in our work, i.e., the Reynolds stress transport equation. In this presentation I show results for both demonstrative examples as well as practical models.

Biography

Ali Mani is an associate professor of Mechanical Engineering at Stanford University. He is also a faculty affiliate of the Institute for Computational and Mathematical Engineering at Stanford. He received his Ph.D. in Mechanical Engineering from Stanford in 2009. Prior to joining the faculty in 2011, he was a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering. His research group builds and utilizes large-scale high-fidelity numerical simulations, as well as methods of applied mathematics, to develop quantitative understanding of transport processes that involve strong coupling with fluid flow and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis. He is the recipient of an Office of Naval Research Young Investigator Award (2015), NSF Career Award (2016), and Tau Beta Pi Teaching Honor Roll (2019).

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