Print Email Facebook Twitter Customized data-driven RANS closures for bi-fidelity LES–RANS optimization Title Customized data-driven RANS closures for bi-fidelity LES–RANS optimization Author Zhang, Yu (Northwestern Polytechnical University) Dwight, R.P. (TU Delft Aerodynamics) Schmelzer, M. (TU Delft Aerodynamics) Gómez, Javier F. (Student TU Delft) Han, Zhong hua (Northwestern Polytechnical University) Hickel, S. (TU Delft Aerodynamics) Date 2021 Abstract 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. Subject Algebraic stress modelLarge-eddy simulationMulti-fidelity optimizationReynolds-averaged Navier-StokesTurbulence modelling To reference this document use: http://resolver.tudelft.nl/uuid:1baba947-8c3b-434b-a3d8-9dd73167fbe9 DOI https://doi.org/10.1016/j.jcp.2021.110153 ISSN 0021-9991 Source Journal of Computational Physics, 432 Part of collection Institutional Repository Document type journal article Rights © 2021 Yu Zhang, R.P. Dwight, M. Schmelzer, Javier F. Gómez, Zhong hua Han, S. Hickel Files PDF 1_s2.0_S0021999121000450_main.pdf 3.1 MB Close viewer /islandora/object/uuid:1baba947-8c3b-434b-a3d8-9dd73167fbe9/datastream/OBJ/view