Improved residual network based on attention mechanism for flame temperature field reconstruction
The method of reconstructing the flame temperature field based on convolutional neural network has been widely used in recent years,but the traditional convolutional neural network model is prone to overfitting or model degradation as the number of network layers increases,resulting in large reconstruction errors.This paper proposed an improved method,which used the ResNet18 network to reconstruct the flame temperature field,and introduced the attention mechanism and local importance pooling to optimize the extracted content,realized the full use of known information,and reduced the reconstruction error.The experimental results showed that after introducing the local importance pooling and attention mechanism at the same time,the average relative error of temperature field reconstruction was 0.13%,and the maximum relative error was 0.75%.Compared with the initial ResNet18 network,the average relative error was reduced by 31.58%.The maximum relative error was reduced by 34.21%.The influence of the two factors on the reconstruction accuracy was verified by ablation experiments.The results showed that the temperature field reconstruction accuracy after adding two improved modules at the same time was better than that after adding a single improved module,and the local importance pooling module had a significant effect on the accuracy improvement.
temperature fieldresidual networkattention mechanismpooling
国家科技期刊平台
NSTL
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