Abstract To determine heat transfer regimes of the pre and post CHF, the SPACE code calculates the wall temperature from a nucleate boiling heat transfer model at the given CHF. It needs iterations and consumes a large amount of computing time. To reduce the calculation time, this paper introduces the application of a machine learning method. Big data of the wall temperature at CHF was built by using the subprogram constructed as is in the SPACE code. Based on that database, the neural network models were trained and two neural network models having different configurations were suggested. The developed neural network models were implemented in the SPACE code and test calculations were performed. The neural network applied SPACE code properly predicted the wall temperature at CHF. In test calculations, the calculation time was also investigated. All suggested neural network models highly enhanced the calculation speed corresponding to a maximum 86% time reduction.

中文翻译：

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使用机器学习模型预测临界热通量下的壁温

摘要 为了确定 CHF 前后的传热方式，SPACE 代码根据给定 CHF 下的核沸腾传热模型计算壁温。它需要迭代并消耗大量计算时间。为了减少计算时间，本文介绍了一种机器学习方法的应用。CHF 壁温的大数据是使用 SPACE 代码中构建的子程序构建的。基于该数据库，训练神经网络模型并建议具有不同配置的两个神经网络模型。开发的神经网络模型在 SPACE 代码中实现，并进行了测试计算。应用 SPACE 代码的神经网络正确预测了 CHF 下的壁温。在测试计算中，还研究了计算时间。