Modeling LWR fuel Rod's gap thickness heat transfer coefficient by artificial neural network technique

During the lifetime of nuclear Light Water Reactors (LWRs), the fuel-cladding gap thickness heat transfer coefficient (hgap) 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 hgap 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 hgap 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 hgap. 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 hgap. [Display omitted] •ANN is used to predict LWRs fuel-cladding gap heat transfer coefficient.•FRAPCON-3.5 code was used to construct approximately 66,000 train data set.•Garson's sensitive analysis is used to determine the relative importance of the inputs.