Prediction of full-coverage gas film cooling performance based on machine learning
The full coverage gas film cooling structure was taken as the research object,and the refined and rapid prediction of temperature field and thermal stress field was realized based on machine learning method.The hole inclination angle,hole compound angle,temperature ratio and blowing ratio were selected as the input of the machine learning model,and the learning samples required by machine learning were established based on Latin hypercube sampling and CFD simulation method.For the prediction structure,independent machine learning grid nodes were divided(the number of grids was far less than that of CFD grid),and a multi-layer perceptron neural network model was established independently on each node to directly predict the temperature/thermal stress of the node.The numerical correlation between the model input and output parameters was analyzed.The results show that the mean square error of the model for predicting the temperature of the solid domain is 41.21 K2,and the mean absolute percentage error is 0.86%.The mean square error of the model for predicting the thermal stress of the solid domain is 38.51 MPa2,and the mean absolute percentage error is 0.15%.The wall temperature decreases with the increase of the compound angle,the blowing ratio and the temperature ratio,and increases with the increase of the hole inclination angle.The thermal stress and the wall temperature have the same trend.The proposed data compression and prediction strategy is designed for each grid node independently,avoiding the node coordinates as the model input,and improving the modeling efficiency.
gas turbinefilm coolingmachine learningneural network
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