Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network

Abstract

The present study introduces a novel optimization framework that combines a Graph Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This framework can optimize the heat sources layout in horizontal annuli, achieving the highest heat transfer efficiency. The predictive GCN surrogate model utilizes graph data as training input and enables it with sensitivity to geometric variations at the node level. This sensitivity allows achieving higher training and predictive accuracy compared to fully connected or convolutional neural networks. The optimization is achieved by GA with global search capabilities seeking the optimal spatial heat sources layout. The predicted average Nusselt numbers of the horizontal annuli by GCN are utilized as evaluation metrics. Simultaneously, geometric parameter features are targeted as optimization objectives, achieving optimization results consistent with numerical results across various cases. Furthermore, the GCN predicts temperature fields with average errors below 1.33% compared to numerical results. Specifically, in the cases validation involving single, double, and triple heat sources, the GCN-GA demonstrates faster optimization speeds, increased by 10, 29, and 110 times compared to numerical methods, respectively. Therefore, significant enhancements in optimization efficiency facilitated by the optimization framework built upon the GCN surrogate model.