Design of New Shielding Materials for Space Reactor Shielding Structure Optimization

Currently, the high density and heavy weight of space radiation shielding materials limit their development and application in the aerospace field. To address this issue, this study proposes a novel fast optimization algorithm for shielding material combinations by integrating Particle Swarm Optimization - Differential Evolution (PSO-DE), Monte Carlo (MC) method, and Back Propagation (BP) prediction model. Among them, the PSO-DE algorithm can be used to search for potential optimal solutions in a vast material parameter space. The MC method can simulate the interaction process between radiation and shielding materials, providing a basis for evaluating shielding effectiveness. The BP model can quickly predict the shielding performance of different material combinations, accelerating the entire optimization process. It was found that under the condition of 15 individuals per generation, different algorithms performed differently.In terms of optimization results, the PSO-DE algorithm achieved the best performance with the shield design, converging to a minimum mass of 25 kg. The Elitist Genetic Algorithm (EGA) significantly improved the results, achieving a minimum mass of over 30 kg. After optimizing the shield design using these algorithms, the total mass of the final solution that met the radiation value constraints was 1,483 kg, a reduction of 17.4174% compared to the initial method. For the BP model constructed, the average deviation percentage (PBIAS) error in predicting sub-annular dose values was controlled within 20%, and the PBIAS error for photon predictions was below 30%. Although the photon error was relatively high due to the incomplete description of the secondary photon generation mechanism, the predicted values were generally higher, effectively describing the decay pattern of photons.This study proposes a new optimization process to achieve precise and rapid optimization of shielding materials, obtain lightweight and efficient shielding solutions, and provide new ideas for the development of aerospace radiation shielding materials.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3610902
Citations
1
Primary Topic
Nuclear and radioactivity studies
Type
article
Field-Weighted Citation Impact
1.30
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article

Design of New Shielding Materials for Space Reactor Shielding Structure Optimization

Xiaomiao Chi, Yongwei Sun, Cao Qinjian, Yi Han
1 citations
IEEE Access
Nuclear and radioactivity studies
1.30
article

Design of New Shielding Materials for Space Reactor Shielding Structure Optimization

Xiaomiao Chi, Yongwei Sun, Cao Qinjian, Yi Han
article en
1 citations

Abstract

Currently, the high density and heavy weight of space radiation shielding materials limit their development and application in the aerospace field. To address this issue, this study proposes a novel fast optimization algorithm for shielding material combinations by integrating Particle Swarm Optimization - Differential Evolution (PSO-DE), Monte Carlo (MC) method, and Back Propagation (BP) prediction model. Among them, the PSO-DE algorithm can be used to search for potential optimal solutions in a vast material parameter space. The MC method can simulate the interaction process between radiation and shielding materials, providing a basis for evaluating shielding effectiveness. The BP model can quickly predict the shielding performance of different material combinations, accelerating the entire optimization process. It was found that under the condition of 15 individuals per generation, different algorithms performed differently.In terms of optimization results, the PSO-DE algorithm achieved the best performance with the shield design, converging to a minimum mass of 25 kg. The Elitist Genetic Algorithm (EGA) significantly improved the results, achieving a minimum mass of over 30 kg. After optimizing the shield design using these algorithms, the total mass of the final solution that met the radiation value constraints was 1,483 kg, a reduction of 17.4174% compared to the initial method. For the BP model constructed, the average deviation percentage (PBIAS) error in predicting sub-annular dose values was controlled within 20%, and the PBIAS error for photon predictions was below 30%. Although the photon error was relatively high due to the incomplete description of the secondary photon generation mechanism, the predicted values were generally higher, effectively describing the decay pattern of photons.This study proposes a new optimization process to achieve precise and rapid optimization of shielding materials, obtain lightweight and efficient shielding solutions, and provide new ideas for the development of aerospace radiation shielding materials.

IEEE AccessVol. 13
National Institute for Radiological Protection (CN)
Openalex Percentile: Top 17%
Nuclear and radioactivity studies
1.30
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