引入物理约束的航空发动机燃烧室温度场预测模型

Quickly obtaining the temperature field of the aero-engine combustor cross-section through the inlet flow parameters can help researchers quickly understand the combustion state of the combustor in the engine, which is more important for the design and optimization of the aero-engine combustor. This paper proposed a fast prediction scheme for the temperature field of an aero-engine combustor based on a deep learning approach. The dual-path network model with an attention module constructed in the article achieved 0.64% average relative deviation in the temperature field of the test sets, which can achieve high-precision prediction of the combustor temperature field. However, in the districts far from the learning conditions, the attention network model trained only with mean square error faces performance degradation. By introducing the physical loss function, the predictive performance of the attention network model is significantly improved, resulting in a 48.4% reduction in the average relative error of the temperature field. Compared with the traditional convolutional network model and fully-connected network, the attention network trained with the physical loss function has better prediction performance both inside and outside the learning edge.