Machine learning methods for turbulence modeling in subsonic flows around airfoils

• Published in: Physics of fluids (1994) Vol. 31; no. 1
• Main Authors: Zhu, Linyang, Zhang, Weiwei, Kou, Jiaqing, Liu, Yilang
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.01.2019
• Subjects: Aerodynamics; Anisotropy; Artificial intelligence; Basis functions; Computational fluid dynamics; Computer simulation; Direct numerical simulation; Drag coefficients; Eddy viscosity; Fluid dynamics; Fluid flow; Fluid mechanics; High Reynolds number; Machine learning; Mapping; Neural networks; Partial differential equations; Physics; Radial basis function; Reynolds number; Skin friction; Subsonic flow; Training; Turbulence models; Viscosity; Vortices
• ISSN: 1070-6631; 1089-7666
• DOI: 10.1063/1.5061693

In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.