Machine-Learning-Based Turbulence Model for Film Cooling
In film cooling,the interaction of the main flow and the jet produces a three-dimensional vortex structure and turbulent mixing,which cause convective and turbulent diffusion transport of energy.Due to the three-dimensional nature of the above phenomenon,the traditional turbulence model cannot accurately predict the film cooling effect,which limits the analysis and design level of the gas turbine cooling structure.In this paper,the inversion machine learning framework for turbulence models is used to correct the turbulent Prandtl number with a spatial distribution based on the temperature data at key positions in the flow field.The developed machine learning turbulent temperature diffusion model can significantly reduce the prediction error of cooling effectiveness and has good generalization ability.