Analysis of the LatticeNet neural network framework's performance using prediction-calculated temperature coefficients in PWR assemblies

Various Machine Learning (ML) techniques have seen recent and growing interest in the creation of surrogate neutronics models as a potential way to avoid the computational expenses associated with conventional high-fidelity modeling. Artificial Neural Networks (ANNs) have been shown to be particularly useful for single-assembly predictions involving pin-wise power distributions and multiplication factors. In this paper, the LatticeNet neural network framework is investigated as a method to predict Doppler and moderator temperature coefficients for Pressurized Water Reactor (PWR) fuel assemblies, as well as differential boron worth. This approach uses the built-in tools developed alongside LatticeNet to construct two fully-connected network architectures capable of predicting k-eigenvalues from two inputs such as fuel enrichment and temperature when trained with data generated with CASMO-4E. A single network taking in all study parameters as inputs was then used to predict k-eigenvalues for fuel temperature, moderator temperature, and boron perturbation cases. The calculated temperature coefficients and differential boron worth values were compared with a bank of reference values to validate the efficacy of this method. Overall, differences in k-eigenvalues were within 0.017% in the worst case. The temperature coefficients saw mean errors of 1.85% and 1.69% for the two-parameter networks, respectively. The all-parameter network was then shown to predict 1100 points in 236 ms compared to the 4.95 min CASMO-4E took to generate them. Additionally, the differential boron worth achieved the lowest mean error of 0.30%; each of these values were within our acceptance criteria.