Application of deep reinforcement learning in parameter optimization and refinement of turbulence models.

Abstract

In the field of computational fluid dynamics, the accuracy of turbulence models is crucial. The aim of this study is to improve the accuracy of simulations by optimizing turbulence model parameters, in order to address the cost and time limitations of traditional wind tunnel tests and on-site measurements. Based on the SST (Shear Stress Transport) k-ω turbulence model, this article proposed a parameter optimization method for turbulence models based on DDPG (Deep Deterministic Policy Gradient). Using wind pressure coefficient (WPC) simulation as an example. Numerical simulation of complex building wind fields was achieved using OpenFOAM software, and sensitivity analysis of model parameters was conducted. Key parameters that significantly affected simulation results were identified, and GPR (Gaussian Process Regression) was established as a surrogate model to fit the initial CFD (Computational Fluid Dynamics) simulation data. The DDPG algorithm was used for parameter optimization, and model parameters were gradually adjusted to minimize wind pressure simulation errors. The experimental results showed that in a single wind direction angle, the average, Root Mean Square (RMS), maximum, and minimum values of the DDPG optimized WPC were closer to the actual WPC. In the set wind direction angle of 0°-50°, the simulated WPC after DDPG optimization was closer to the actual WPC than before optimization. The DDPG optimization method significantly reduced the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the WPC, and its optimization effect was significantly better than the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) methods.