A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models

In this work, the presence of the BCC phase and its thermal conductivity in uranium-based metallic nuclear fuels at higher temperatures were predicted by coupling a random forest classification model with a multilayer perceptron network model. To analyze the effect of phase constitution on thermal conductivity in uranium-based alloys, the pearson correlation coefficient was introduced. The data samples in the current small-sample database of metallic nuclear fuels, created by collecting experimental and computational data from the literature, were first pre-processed with the guide of professional knowledge before machine learning, involving data screening and data completion. Based on temperature and composition characteristics, the existence of the BCC phases and the thermal conductivities of the candidates were predicted by our models. The optimized thermal conductivities of the candidate alloys coincide well with the experimental results, which shows the reliability of our model.