Customized data-driven RANS closures for bi-fidelity LES–RANS optimization

Autor: Stefan Hickel, Javier F. Gómez, Martin Schmelzer, Zhong-Hua Han, Yu Zhang, Richard P. Dwight
Rok vydání: 2021
Předmět:
Physics and Astronomy (miscellaneous)
Computer science
FOS: Physical sciences
010103 numerical & computational mathematics
Reynolds-averaged Navier-Stokes
01 natural sciences
Data-driven
Physics::Fluid Dynamics
Turbulence modelling
Large-eddy simulation
Algebraic stress model
Applied mathematics
Point (geometry)
Shape optimization
0101 mathematics
ComputingMethodologies_COMPUTERGRAPHICS
Multi-fidelity optimization
Numerical Analysis
Turbulence
Applied Mathematics
Fluid Dynamics (physics.flu-dyn)
Physics - Fluid Dynamics
Computational Physics (physics.comp-ph)
Computer Science Applications
010101 applied mathematics
Computational Mathematics
Closure (computer programming)
Modeling and Simulation
Turbulence kinetic energy
Reynolds-averaged Navier–Stokes equations
Physics - Computational Physics
Large eddy simulation
Zdroj: Journal of Computational Physics, 432
ISSN: 0021-9991
DOI: 10.1016/j.jcp.2021.110153
Popis: Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low- and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for turbulent fluid-flow applications with Large-Eddy Simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) as the high- and low-fidelity models within a hierarchical-Kriging surrogate modelling framework. Since the LES–RANS correlation is often poor, we use the full LES flow-field at a single point in the design space to derive a custom-tailored RANS closure model that reproduces the LES at that point. This is achieved with machine-learning techniques, specifically sparse regression to obtain high corrections of the turbulence anisotropy tensor and the production of turbulence kinetic energy as functions of the RANS mean-flow. The LES–RANS correlation is dramatically improved throughout the design-space. We demonstrate the effectivity and efficiency of our method in a proof-of-concept shape optimization of the well-known periodic-hill case. Standard RANS models perform poorly in this case, whereas our method converges to the LES-optimum with only two LES samples.
Databáze: OpenAIRE
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