| Autor: |
Zhang, Yu, Dwight, Richard P., Schmelzer, Martin, Gomez, Javier F., Hickel, Stefan, Han, Zhong-hua |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Journal of Computational Physics 2021 |
| Druh dokumentu: |
Working Paper |
| 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 effectiveness 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: |
arXiv |
| Externí odkaz: |
|
