Enhancing Generalization in Endwall Film Cooling Prediction: Incorporating the Superposition Principle into Transformer-Based Neural Operators

This paper presents a physics-enhanced neural operator framework for predicting turbine endwall temperature fields with a variable number of film holes. The proposed superposition-based deep neural operator divides the prediction process into two stages and incorporates the film-cooling superposition principle into a transformer-based architecture.