AI-Powered Convolutional Neural Network Surrogate Modeling for High-Speed Finite Element Analysis in the NPPs Fuel Performance Framework

Convolutional Neural Networks (CNNs) are proposed for use in the nuclear power plant domain as surrogate models to enhance the computational efficiency of finite element analyses in simulating nuclear fuel behavior under varying conditions. The dataset comprises 3D fuel pellet FE models and involves 13 input features, such as pressure, Young’s modulus, and temperature. CNNs predict outcomes like displacement, von Mises stress, and creep strain from these inputs, significantly reducing the simulation time from several seconds per analysis to approximately one second. The data are normalized using local and global min–max scaling to maintain consistency across inputs and outputs, facilitating accurate model learning. The CNN architecture includes multiple dense, reshaping, and transpose convolution layers, optimized through a brute-force hyperparameter tuning process and validated using a 5-fold cross-validation approach. The study employs the Adam optimizer, with a significant reduction in computational time highlighted using a GPU, which outperforms traditional CPUs significantly in training speed. The findings suggest that integrating CNN models into nuclear fuel analysis can drastically reduce computational times while maintaining accuracy, making them valuable for real-time monitoring and decision-making within nuclear power plant operations.