Application of machine learning algorithm in the prediction of fuel rod temperature performance

Abstract

Abstract: In order to predict the fuel rod temperature performance effectively, we establish a series of machine learning models based on k-nearest neighbor, decision tree and AdaBoost algorithms. The input parameters and calculation results of fuel rod performance analysis software JASMINE and data feature engineering are used as the training and test data for the machine learning models. The models are trained by the training data set which includes the characteristic parameters such as pellet and cladding type, axial height, local power, cladding corrosion thickness and core inlet temperature. After training, the models use the test data to predict the cladding outside surface temperature and pellet center temperature. The prediction results show that the model based on AdaBoost algorithm has the best prediction performances, and the mean square errors of cladding outside surface temperature and pellet center temperature are 0. 605 °C and 8. 347 °C, respectively, and the average absolute errors are 0. 273 °C and 3. 814 °C , respectively. Comparing the predicted values with the target values, the maximum deviation of Adaboost algorithm for the cladding outside surface temperature is 3 °C, and the most of the prediction deviation of the pellet center temperature is less than 10 °C, indicating that the model based on AdaBoost algorithm has the high prediction accuracy for the temperature performance of fuel rods.