A Quasi–Monte Carlo Method With Krylov Linear Solvers for Multigroup Neutron Transport Simulations

Abstract

The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25-26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. A large portion of the meeting included candid discussions of direct experiences with approaches that have and have not worked. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. Problem specifications and results will eventually be hosted in a public repository and will be open to submissions by all Monte Carlo transport codes capable of running the benchmark problem. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits.

Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development

“…MC/DC-enabled explorations into dynamic neutron transport algorithms have been successful, including quasi-Monte Carlo techniques (Pasmann et al, 2023a), hybrid iterative techniques for k-eigenvalue simulations (Pasmann et al, 2023b), population control techniques (Variansyah & McClarren, 2022a;Variansyah & McClarren, 2022b), continuous geometry movement techniques that model transient elements ) (e.g., control rods or pulsed neutron experiments) more accurately than step functions typically used by other codes, initial condition sampling technique for typical reactor transients , hash-based random number generation (B. S. Cuneo & Variansyah, 2024), uncertainty and global sensitivity analysis (K. Clements et al, 2023;K. B. Clements et al, 2024), residual Monte Carlo methods, and machine learning techniques for dynamic node scheduling, among others.…”