【作者】 雷鸣； 蔡文超； 刘鹏； 韩良文； 潘瑞安； 夏星汉；

【Author】 LEI Ming;CAI Wen-chao;LIU Peng;HAN Liang-wen;PAN Rui-an;XIA Xing-han;Nuclear Power Institute of China;

【机构】 中国核动力研究设计院；

【摘要】 高通量多功能低温低压研究堆多采用管壳式换热器进行热量交换。由于二回路直接以河水作为热阱,热交换器传热管内二次侧的堵塞与结垢恶化引起一回路水温异常升高,是研究堆运行维护经常遇到的问题。为了准确实时监测换热器二次侧结垢与堵塞状态变化,本文建立了高通量工程试验堆(HFETR)的管壳式换热器传热管内二次侧结垢平均厚度、二次侧等效传热管流道堵塞数目与换热器运行监测的热工参数(一次水进出口温度、二次水进出口温度、换热器一次侧热功率、二次侧压差,二次水流量)之间的一维简化模型。利用动量BP(误差反向传播)神经网络对基于该模型条件下一年来不同时刻HFETR换热器二次侧平均结垢厚度与二次侧等效传热管流道堵塞数目的求解进行训练学习,将训练好的神经网络进行测试,并对测试结果与HFETR换热器各炉段运行历史及换热器二次侧清理措施的记录进行对比验证。计算结果表明,经过训练神经网络模型计算结果与期望值一致,可以准确实现HFETR运行期间换热器结垢与堵塞状态变化的在线监测。该方法可为高通量研究堆热交换器运行状态的智能监测提供参考。更多还原

【Abstract】 Shell and tube heat exchangers are often used for heat exchange in low temperature and low pressure,high flux multifunctional research reactors.It is a common problem in the operation and maintenance of research reactor that the abnormal rise of water temperature in the primary loop is caused by the fouling and plugging in the secondary side of the heat exchanger tube cooled with river water.In order to accurately and real-time monitor the variation of fouling and blockage in the secondary side of the heat exchanger,the relationship model from the average thickness of fouling and the number of equivalent blockage in the secondary side of the heat transfer tube to thermal parameters(inlet and outlet temperature of primary water,inlet and outlet temperature of secondary water,reactor power and secondary water flow) of the heat exchanger were established.Thermal parameters monitoring combined with the momentum BP(error back propagation) eural network is used to monitor the thickness of the secondary side fouling and the number of blockage in the heat transfer tubes based on the relationship model.The results show that the calculated results based on the trained neural network model are consistent with the expected value of the engineering practices.This method can provide a reference for intelligent monitoring of heat exchanger operation in high flux research reactor.更多还原