开发一种基于机器学习的新型自适应性重新采样算法,用于核数据处理
Alexander Hashemi1, Rafael Macián-Juan2, Martin Ohlerich3
1Chair of Nuclear Engineering, Technical University of Munich (TUM), 85748, Garching, Munich, Germany. alexander.hashemi@tum.de.
Scientific reports
|September 17, 2025
概括
这项研究引入了用于高效核截面数据处理的机器学习,减少低效率并加快反应堆物理和安全的模拟.
科学领域:
- 核工程 核工程是指核工程.
- 计算物理 计算物理
- 数据科学数据科学数据科学
背景情况:
- 核截面数据的高效处理对于反应堆物理和安全至关重要.
- 目前使用层次数据格式版本5 (HDF5) 和NJOY中的ACE等中间文件的方法效率低下.
- 传统的工作流程阻碍了核数据库的直接修改.
研究的目的:
- 开发新的计算技术,以简化核数据处理和修改.
- 消除核数据工作流中的中间文件格式相关的低效率.
- 为了能够直接编辑与OpenMC兼容的核数据库.
主要方法:
- 一种基于机器学习的重抽样算法,用于HDF5格式的核截面数据.
- 一种适应性重新采样策略,以保护共振结构和值行为.
- 一个直接的HDF5修改框架,绕过了旧文件转换.
主要成果:
- 基于ML的重新采样智能地保留了关键数据点,减少了冗余.
- 直接HDF5修改框架允许直接编辑核数据库.
- 基准测试表明,与KNN和高斯过程相比,ML方法提供了更低的误差和更高的计算效率.
结论:
- 综合框架显著改善了核数据的可访问性.
- 这种方法加快了模拟,反应堆核心设计和不确定性量化.
- 新技术简化了核数据处理,用于先进的反应堆应用.
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