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ThinkMind // SIMUL 2025, The Seventeenth International Conference on Advances in System Modeling and Simulation // View article simul_2025_1_50_50032
Feasibility Study of Simplification of Radiation Source Shape Using Monte Carlo N-Particle Transport (MCNP)
 Authors:
Changyeon Yoon Keywords: Source Simplification, Monte Carlo Simulation Abstract:
During the decommissioning of nuclear power plants, radiation exposure to workers can result not only from point sources but also from line sources and volume sources. In particular, radiation sources such as fluids within piping systems can vary over time and space, making it necessary to approximate such sources as point sources in order to ensure the accuracy of dose prediction algorithms. This study aims to quantitatively evaluate the radiation dose rate errors that occur when line sources are modeled as arrays of discrete point sources and to validate this approach through Monte Carlo simulations using the MCNP code. Theoretical calculations were first performed based on a Cs-137 source, and dose rates were calculated according to the location of the measurement point and its distance from the source. The analysis considered various positions such as the center, end, and exterior of the line source. It was confirmed that the relative error decreased to below 10% when the measurement point was located at a distance of 0.5 to 0.9 times the source length, and fell within 1% beyond a distance three times the source length. Additionally, MCNP simulations confirmed that the relative error remained below 10% when the measurement point was located at a distance equal to the length of the line source. These results demonstrate that line sources can be effectively approximated by point sources beyond a certain distance while maintaining sufficiently low error levels. This satisfies the point-source discretization criterion of approximately 1-foot intervals proposed by the EPRI algorithm. The findings of this study offer a practical basis for modeling line sources as point sources in decommissioning scenarios and are expected to contribute to the development of more accurate radiation dose prediction algorithms. Pages: 28 to 30 Copyright: Copyright (c) IARIA, 2025 Publication date: September 28, 2025 Published in: conference ISSN: 2308-4537 ISBN: 978-1-68558-300-2 Location: Lisbon, Portugal Dates: from September 28, 2025 to October 2, 2025
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