Machine learning methods for turbulence modeling in subsonic flows around airfoils
By Linyang Zhu, Weiwei Zhang, Jiaqing Kou, Yilang Liu. The Physics of Fluids, 2018
315 citations recorded by the source corpus.
Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism and large discrepancies between models reduce the reliability of simulating complex flows. Therefore, in recent years, data-driven turbulence model has aroused widespread concern in fluid mechanics. Based on the experimental/numerical simulation results, this approach aims to modify or construct the turbulence model for specific purposes by machine learning technologies. In this paper, we take the results calculated by SA model as training data. Different from low Reynolds number turbulent flows, the data from high Reynolds number flows shows an apparent scaling effect, thus leading to difficulties in the data-driven modeling. In order to improve the fitting accuracy, we divided the flow field into near-wall region, wake region, and far-field region, and built individual model for every region. In this paper, we adopted the radial basis function neural network (RBFNN) and some auxiliary optimization algorithms to reconstruct a mapping function between mean variables and the eddy viscosity. Since this model reflects the relationship between local flow characteristics and turbulent eddy viscosity, it is independent on the airfoil shape and flow condition. The training data in this paper is generated from only three subsonic flow calculations of NACA0012 airfoil. By coupling the proposed approach with N-S equations, we calculated various flow cases as well as two different airfoils and showed the eddy viscosity contours, velocity profiles along the normal direction of wall and skin friction coefficient distributions, etc. Compared with the SA model, the results show a reasonable accuracy and better efficiency, which indicates the positive prospect of data-driven methods in turbulence modeling.
Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/8a24be668fac77d1a7a79191c97d72418b7ea75b
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In Aerospace Engineering
In Computational Mechanics
In Statistical and Nonlinear Physics
Citations in the corpus
- Geometric Deep Learning for Volumetric Computational Fluid Dynamics (2019, background): “2 Fluid side From the fluid side, the main efforts are made to improve existing turbulent model, or generate new ones, to help getting closer to the real Navier-Stokes equations [39] [40] [41] [42] (modeling of near wall turbulent field).”