Abstract During the lifetime of nuclear Light Water Reactors (LWRs), the fuel-cladding gap thickness heat transfer coefficient ( h g a p ) is the most crucial parameter of fuel rod performance that determines the fuel rod thermo-mechanical behavior as well as the maximum level of fuel burn-up during the normal operation. The main objective of the present work is to develop a smart calculative method by using the artificial neural network (ANN) technique to predict the h g a p for a fuel rod of LWRs. The parameters of ANN input include those of fuel design gap initial thickness and pressure, operation time, Linear Heat Generation (LHG), and the average fuel burn-up while h g a p is considered as the only output parameter. The obtained results of FRAPCON-3.5 steady-state fuel rod performance code is used to form the training data set. A Multi-Layer Perceptron feed-forward ANN with one hidden layer is trained with the Levenberg–Marquardt training algorithm. It has been illustrated that the created artificial network can accurately predict the h g a p . Through the weight matrix of the ANN, Garson's sensitive analysis procedure shows the significant role of fuel burn-up in determining the level of h g a p .

中文翻译：

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用人工神经网络技术模拟轻水堆燃料棒间隙厚度传热系数

摘要 在轻水核反应堆（LWR）的寿期内，燃料包壳间隙厚度传热系数（ hgap ）是燃料棒性能最关键的参数，它决定了燃料棒的热机械行为和最大水平。正常运行时燃料燃烧。目前工作的主要目标是通过使用人工神经网络 (ANN) 技术开发一种智能计算方法来预测轻水堆燃料棒的 hgap。人工神经网络输入的参数包括燃料设计间隙初始厚度和压力、运行时间、线性发热（LHG）和平均燃料燃耗，而 hgap 被认为是唯一的输出参数。FRAPCON-3.5稳态燃料棒性能代码得到的结果用于形成训练数据集。具有一个隐藏层的多层感知器前馈 ANN 使用 Levenberg-Marquardt 训练算法进行训练。已经说明创建的人工网络可以准确地预测 hgap 。通过 ANN 的权重矩阵，Garson 的敏感分析程序显示了燃料燃烧在确定 hgap 水平方面的重要作用。