A data-driven adaptive Reynolds-averaged Navier-Stokes k-ω model for turbulent flow

JOURNAL OF COMPUTATIONAL PHYSICS

卷:345

Article

Li, Zhiyong¹  Zhang, Huaibao^1,2  Bailey, Sean C. C.¹  Hoagg, Jesse B.¹  Martin, Alexandre¹ 

[1] Univ Kentucky, Dept Mech Engn, Lexington, KY 40506 USA

[2] Sun Yat Sen Univ, Sch Phys, Guangzhou, Guangdong, Peoples R China

关键词: k-omega;    Adaptive;    Turbulence;    Data-driven;    RANS;   

DOI  :  10.1016/j.jcp.2017.05.009

来源: Elsevier

【 摘 要 】

This paper presents a new data-driven adaptive computational model for simulating turbulent flow, where partial-but-incomplete measurement data is available. The model automatically adjusts the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k-omega turbulence equations to improve agreement between the simulated flow and the measurements. This data-driven adaptive RANS k-omega (D-DARK) model is validated with 3 canonical flow geometries: pipe flow, backward-facing step, and flow around an airfoil. For all test cases, the D-DARK model improves agreement with experimental data in comparison to the results from a non-adaptive RANS k-omega model that uses standard values of the closure coefficients. For the pipe flow, adaptation is driven by mean stream-wise velocity data from 42 measurement locations along the pipe radius, and the D-DARK model reduces the average error from 5.2% to 1.1%. For the 2-dimensional backward-facing step, adaptation is driven by mean stream-wise velocity data from 100 measurement locations at 4 cross-sections of the flow. In this case, D-DARK reduces the average error from 40% to 12%. For the NACA 0012 airfoil, adaptation is driven by surface-pressure data at 25 measurement locations. The D-DARK model reduces the average error in surface-pressure coefficients from 45% to 12%. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free