评估机器学习的核数据精度在完整的核心核反应堆蒙特卡洛中子和计算效率分析中子学
Alexander Hashemi1, Rafael Macián-Juan2, Martin Ohlerich3
1Chair of Nuclear Engineering, Technical University of Munich (TUM), Garching, 85748, Munich, Germany. alexander.hashemi@tum.de.
Scientific reports
|January 9, 2026
概括
一种新的机器学习方法显著加快了用于蒙特卡洛模拟的核数据处理. 这种方法减少了计算资源,同时保持了核反应堆分析的高精度.
科学领域:
- 核工程 核工程是指核工程.
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 连续能量核数据对于准确的反应堆模拟至关重要.
- 处理大型核数据库需要大量的计算资源.
- 现有的核数据缩小方法可能会损害模拟准确性.
研究的目的:
- 开发和评估一种基于机器学习的方法来减少核数据.
- 评估数据减少对蒙特卡洛关键性和燃烧分析的影响.
- 量化减少核数据的性能增长和保真性保护.
主要方法:
- 经过修改的ENDF/B-VII.1层次数据格式,版本5 (HDF5) 核数据文件使用机器学习.
- 保留了23个核素的10-50%的核数据,保留了核数据的关键特征.
- 使用OpenMC对EPR和VVER-1000完整核心模型对原始数据进行比较,对减少的库进行了比较.
主要成果:
- 实现显著减少墙壁时间 (17.81%的EPR,42.5%的VVER-1000) 和峰值内存使用.
- 保持了高保真度,对于VVER-1000来说,keff的最大绝对差异在96.79 pcm内.
- 报告了反应速率和循环结束时核化物库存的相对差异最小.
结论:
- 开发的机器学习减少方法有效地加速了整个核心分析.
- 该方法成功地降低了计算需求 (MaxRSS),同时保持了中子学研究的真实性.
- 这种方法为高效准确的核反应堆模拟提供了一个有希望的途径.
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