基于物理信息神经网络的气膜冷却湍流模型反演学习

PINN-based Inversion Learning of Turbulence Model for Film Cooling

由于气膜冷却问题中湍流的复杂特性,传统雷诺平均(RANS)方法会低估湍流的热扩散强度,导致冷却效果计算不准确。对此提出了一套基于物理信息神经网络(PINN)的湍流建模框架,基于RANS流场和大涡模拟(LES)温度场,建立了数据驱动的湍流普朗特数神经网络模型,在RANS求解器中嵌入该模型,可以动态调整湍流的热扩散强度,获得了与LES高度一致的温度场。结果表明:PINN是构建数据驱动湍流模型的良好方法,对于湍流普朗特数的建模可以有效提升RANS方法对温度预测的准确性。

Due to the complexity of turbulent flow problems for film cooling,the traditional Reynolds average Navier-Stokes(RANS)method tends to underestimate the intensity of turbulent thermal diffusion,leading to inaccurate prediction of cooling effectiveness.A framework based on physics-informed neural network(PINN)was therefore proposed,and a data-driven neural network model of turbulent Prandtl number was built based on RANS flow data and large eddy simulation(LES)temperature data.After implementing this model into a RANS solver,the intensity of turbulent thermal diffusion could be adjusted dynamically and a temperature distribution highly consistent with LES results was obtained.Results show that PINN is an effective method to build a data-driven turbulence model and modeling of turbulent Prandtl number can effectively improve the accuracy of RANS temperature prediction.