Research on Heat Pipe Reactor Startup Process based on Autonomous Operation
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摘要: 热管反应堆(HPR)的应用对无人自主运行技术提出了迫切需求,将自主运行技术应用于HPR,可实现状态感知、趋势预测、策略优化;能够有效避免人因失误;提升HPR技术性能、拓展核动力应用领域。以MegaPower热管堆为研究对象,以HPRTRAN程序为分析工具,针对热管堆运行过程中的重要组成——HPR启堆过程开展基于自主运行的研究,建立了适用于HPR启堆的、由监测诊断层-预测层-决策层组成的自主运行框架,初步开发了HPR自主运行系统。研究结果表明,自主运行系统预测结果准确性较高,决策方案较为科学且具备一定可行性。相关研究成果可为后续全面实现HPR无人值守自主运行奠定基础。
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关键词:
- 自主运行 /
- 热管反应堆(HPR) /
- 启堆过程 /
- 智能算法 /
- 决策
Abstract: The application of the heat pipe reactor (HPR) urgently requires unmanned autonomous operation technology. Applying autonomous operation technology to HPR can realize state sensing, trend prediction and strategy optimization, which can effectively avoid human errors, improve the technical performance of HPR and expand the application fields of nuclear power. In this paper, the MegaPower reactor was used as the research object and the HPRTRAN program was used as the analysis tool to carry out an autonomous operation research based on the HPR start-up process, which is an important component of the HPR operation process, and then an autonomous operation framework consisting of monitoring and diagnosis layer, prediction layer and decision layer was established for the HPR start-up. The research results show that the prediction results of the autonomous operation system are highly accurate and the decision-making scheme is scientific and feasible. The research results can lay a foundation for the subsequent full realization of the unattended autonomous operation of the HPR. -
表 1 输入参数的取值范围和分布
Table 1. Range and Distribution of the Input Parameters
参数 取值范围 分布 单次旋转控制鼓角度 (0.5°, 5.5°) 每隔0.5°离散取值 旋转速度/[(°)·min−1] (10, 60) 均匀分布 最小观察时间/s (1, 20) 温度稳定判据/K (1, 30) 功率稳定判据 (0.003, 0.1) 参数名 降偏差 降方差 数据集 异常点处理
扩大数据集异常点处理
扩大数据集算法 增加算法复杂度
集成学习降低算法复杂度
集成学习表 3 集成回归算法比较
Table 3. Comparison of Ensemble Regression Algorithm
算法 基回归
器类型效果 集成方式 多样性处理 Bagging 同质 降方差 加权平均 训练集抽样+属性集抽样 Boosting 同质 降偏差 加权平均 训练集分布调整 Stacking 异质 降方差 再学习 不同基回归器+训练集抽样 Voting 异质 降方差 加权平均 不同基回归器 -
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