Work

Distributed Memory Algorithms for Weight Cancellation in Monte Carlo Particle Transport Simulations

Year: 2024

Type: article

Abstract: \nRecent literature has demonstrated use cases for Monte Carlo transport simulations where particles can have statistical weights that are positive or negative. There are even examples which require particles to have complex statistical weights, and the real and imaginary components can be positive or negative. In such cases, weight cancellation algorithms can be very efficient at reducing the variance, or might even be required for a simulation to converge. Previous works that have employed weight cancellation in distributed memory simulations required that all fission particles be sent to a single node for the cancellation operation. This work examines possible implementations of distributed memory weight cancellation algorithms that do not require the transfer of the fission source to a single node.\n

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/epjconf/202430209007/pdf

Authors: Nicholas Grablevsky, Hunter Belanger

Institution: Rensselaer Polytechnic Institute

Language: English

Topic: Nuclear reactor physics and engineering

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, National Energy Research Scientific Computing Center, Office of Science, Advanced Scientific Computing Research

OpenAlex ID: W6903094660

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