Study of a Three-way Coupling Calculation Method Based on Particle Transport-Activation Calculation-Intelligent Optimization
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摘要: 现有屏蔽设计优化技术通常依赖设计者的经验,其效率低,不确定性大。为了提高屏蔽设计效率,本文结合多目标优化算法和辐射屏蔽计算开展屏蔽设计,开发了基于粒子输运-活化计算-智能优化的三向耦合计算程序,并基于本文构造的屏蔽计算模型进行了验证。数值计算结果表明,基于离散纵标方法和遗传算法的屏蔽设计优化方法可以实现屏蔽材料体积、屏蔽材料质量、停堆后活化剂量率和正常运行剂量率等多个目标的同时优化。Abstract: Existing shielding design optimization techniques usually rely on the experience of the designer, which is inefficient and has high uncertainty. In order to improve the efficiency of shielding design, this paper combines the multi-objective optimization algorithm and radiation shielding calculation to carry out shielding design, and develops a three-way coupling calculation code based on particle transport-activation calculation-intelligent optimization. The code is validated using the shielding calculation model constructed in this study. The numerical results show that the shielding design optimization method based on the discrete ordinate method and genetic algorithm can achieve the simultaneous optimization of multiple objectives, such as shielding volume, weight, activation dose rate after reactor shutdown and normal operation dose rate.
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Key words:
- Discrete ordinate /
- Genetic algorithm /
- Multi-objective optimization /
- Shielding design
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表 1 随机生成的屏蔽方案
Table 1. Randomly Generated Shielding Scheme
方案 厚度
/cm质量
/t停堆后活化
剂量率
/(mSv·h−1)正常运行
剂量率
/(mSv·h−1)1 62.8 39.1 1.45×104 4.00×104 2 70.4 48.8 4.94×103 1.52×104 3 74.7 52.9 2.58×103 1.01×104 4 76.5 43.5 2.72×103 9.02×103 5 78.1 53.9 1.92×103 6.63×103 6 79.5 53.5 1.69×103 5.72×103 7 81.7 56.2 1.21×103 5.11×103 8 86.3 43.8 1.13×103 5.51×103 9 93.4 57.8 3.91×102 1.74×103 表 2 最终优化得到的屏蔽方案
Table 2. Final Optimized Shielding Scheme
方案 厚度
/cm质量
/t停堆后活化
剂量率
/(mSv·h−1)正常运行
剂量率
/(mSv·h−1)1 62.8 29.7 2.36×104 50.8×103 2 77.4 45.7 1.99×103 7.16×103 3 79.9 40.6 2.43×103 9.24×103 4 81.0 51.0 1.40×103 4.82×103 5 83.2 55.7 1.12×103 4.00×103 6 83.2 47.4 1.23×103 4.82×103 7 86.3 52.5 7.13×102 2.93×103 8 87.0 49.4 5.93×102 3.07×103 9 88.6 56.1 6.04×102 2.33×103 10 90.6 53.4 3.68×102 2.00×103 11 91.5 70.6 3.57×102 1.63×103 12 93.7 41.3 1.76×103 8.58×103 13 96.2 55.3 2.91×102 1.64×103 14 97.4 53.8 3.29×102 1.99×103 15 97.6 56.7 2.11×102 1.30×103 16 100.0 53.1 2.37×102 1.80×103 表 3 最终优化得到的屏蔽方案厚度
Table 3. Thickness of the Final Optimized Shielding Scheme
方案 水箱厚度/cm 铅厚度/cm 不锈钢4厚度/cm 13 83.9 10.6 1.7 14 85.8 9.5 2.1 15 85.1 11.2 1.3 -
[1] 胡华四,许浒,张国光,等. 新型核辐射屏蔽材料的优化设计[J]. 原子能科学技术,2005, 39(4): 363-366. doi: 10.3969/j.issn.1000-6931.2005.04.018 [2] 廖伶元,邱小平. 屏蔽材料组分含量的优化设计[J]. 核电子学与探测技术,2010, 30(1): 118-120. doi: 10.3969/j.issn.0258-0934.2010.01.029 [3] 杨寿海,陈义学,王伟金,等. 多目标辐射屏蔽优化设计方法[J]. 原子能科学技术,2012, 46(1): 79-83. doi: 10.7538/yzk.2012.46.01.0079 [4] 应栋川,肖锋,张宏越,等. 基于遗传算法的核反应堆辐射屏蔽优化方法研究[J]. 核动力工程,2016, 37(4): 160-164. [5] 李晓玲,余方伟,孙霖,等. 铅硼聚乙烯复合屏蔽材料成分配比优化设计[J]. 舰船科学技术,2015, 37(12): 148-154. doi: 10.3404/j.issn.1672-7649.2015.12.031 [6] 宋英明,赵云彪,李鑫祥,等. 一种用于船用反应堆屏蔽结构优化的方法[J]. 核科学与工程,2017, 37(3): 355-361. doi: 10.3969/j.issn.0258-0918.2017.03.003 [7] 韩文敏,戴耀东,姚初清,等. 遗传算法在中子-γ混合辐射场屏蔽材料优化设计中的应用[J]. 计算物理,2024, 41(3): 357-366. [8] 雷德明,严新平. 多目标智能优化算法及其应用[M]. 北京: 科学出版社,2009: 31-32. [9] 陈伯显,张智,杨祎罡. 核辐射物理及探测学[M]. 第二版. 哈尔滨: 哈尔滨工程大学出版社,2021, 97. [10] American Nuclear Society. American national standard neutron and gamma-ray flux-to-dose-rate factors: ANSI/ANS-6.1. 1-1977[R]. La Grange Park: American Nuclear Society, 1977. -