System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

核安全分析采用系统热工水力（STH）规范。临界流动模型 (CFM) 对 STH 模拟的准确性非常重要。为了克服当前 CFM 的缺陷（精度低或计算时间长），本文开发了一种基于遗传神经网络（GNN）的 CFM。为了建立一个强大的模型，除了临界质量通量外，该模型还考虑了临界压力和临界质量，这是以前很少考虑的。与传统的均质平衡模型（HEM）和穆迪模型相比，GNN模型可以更准确地预测临界质量通量（约80%的结果在±20%的误差范围内）；与 Leung 模型和 Shannak 模型进行临界压力预测相比，GNN 模型取得了最好的结果（超过 80% 的预测结果在 ±20% 的误差范围内）。对于关键质量，可以达到类似的精度。本工作中基于 GNN 的 CFM 对 STH 代码 CFM 开发具有重要意义。