Prediction of Neutronics Parameters Within a Two-Dimensional Reflective PWR Assembly Using Deep Learning

Traditional light water reactor simulations are usually either high fidelity, requiring hundreds of node-hours, or low fidelity, requiring only seconds to run on a common workstation. In current research, it is desirable to combine the positive aspects of both of these simulation types while minimizing their associated negative costs. Because neural networks have shown significant success when applied to other fields, they could provide a means for combining these two classes of simulation. This paper describes a methodology for designing and training neural networks to predict normalized pin powers and keff within a reflective two-dimensional pressurized water reactor assembly model. The developed methodology combines computer vision approaches, modular neural network approaches, and hyperparameter optimization methods to intelligently design novel network architectures. This methodology has been used to develop a novel new architecture, LatticeNet, which is capable of predicting pin-resolved powers and keff at a high level of detail. The results produced by this novel architecture show the successful prediction of the target neutronics parameters under a variety of typical neutronics conditions, and they indicate a potential path forward for neural network–based model development.