Data-Driven Turbulence Modeling for Film Cooling Heat Transport, Part II: Modeling and Evaluation

In film cooling problems, the turbulent heat transport and the resultant cooling effectiveness distribution are difficult to predict for traditional Reynolds-averaged Navier-Stokes (RANS) methods. In this paper, a data-driven model of the turbulent Prandtl number is built based on the Physics Informed Neural Network (PINN), which trains the turbulence model based on flow data from RANS and temperature data from Large Eddy Simulation (LES). PINN is used as an efficient inverse solver to get turbulence information from the mean flow data such as velocity and temperature and also as a model trainer to train the algebraic turbulence model at the same time. Accelerated by modern GPUs, the PINN method has been a powerful competitor to the adjoint method and the ensemble Kalman filter to solve inverse problems. The obtained data-driven model can adjust the intensity of turbulent heat transport and thus reduce the error of the traditional RANS method. After validation, the model shows good generalization ability when the hole geometry and the blowing ratio vary.