Abstract
Reynolds-stress closure modeling is critical to Reynolds-averaged Navier-Stokes (RANS) analysis, and it remains a challenging issue in reducing both structural and parametric inaccuracies. This study first proposes a novel algebraic stress model named as tensorial quadratic eddy-viscosity model (TQEVM), in which nonlinear terms improve previous model-form failure due to neglection of nonlocal effects. Then a data-driven regression model based on a fully-connected deep neural network is designed to determine the TQEVM coefficients. The well-trained data-driven model using high-fidelity direct numerical simulation (DNS) data successfully learned the underlying input-output relationships, further obtaining spatial-dependent optimal values of these coefficients. Finally, detailed validations are made in wall-bounded flows where nonlocal effects are expected to be significant. Comparative results indicate that TQEVM provides improvements both for the stress-strain misalignment and stress anisotropy, which are clear advantages over linear and quadratic eddy-viscosity models. TQEVM extends to the scope of resolution to the wall distance y + ≈ 9 as well as provides a realizable solution. RANS simulations with TQEVM are also carried out and the obtained mean-flow quantities of interest agree well with DNS. This work, therefore, results in a high-fidelity representation of Reynolds stresses and contributes to further understanding of machine-learning-assisted turbulence modeling and regression analysis.
Highlights
There are many kinds of turbulence phenomena in energy engineering, e.g., the wake meandering.To understand these complicated flow behavior, turbulence simulation and modeling is an important and useful tool, which has been extensively investigated and comprehensively reviewed [1,2,3,4,5]. more accurate scale-resolving simulations, e.g., large-eddy simulation (LES) and direct numerical simulation (DNS), are increasingly popular, Reynolds-averaged Navier-Stokes (RANS)analysis remains a widely used option in computational fluid dynamics due to its efficiency [3] and is expanding into new stages [5,6]
As a resulting in the large stress-strain misalignment due to nonlocal effects induced by strong result, the nonlinear terms in tensorial quadratic eddy-viscosity model (TQEVM)
We present a machine learning strategy to assist the development of RANS
Summary
Key Lab of Structures Dynamic Behavior and Control of Ministry of Education, Harbin Institute of Technology, Harbin 150090, China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China Received: 4 December 2019; Accepted: 30 December 2019; Published: 4 January 2020
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