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首页> 外文期刊>Nuclear engineering and technology >PREDICTION OF DIAMETRAL CREEP FOR PRESSURE TUBES OF A PRESSURIZED HEAVY WATER REACTOR USING DATA BASED MODELING
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PREDICTION OF DIAMETRAL CREEP FOR PRESSURE TUBES OF A PRESSURIZED HEAVY WATER REACTOR USING DATA BASED MODELING

机译:基于数据的建模预测加压重水反应堆压力管的径向蠕变

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The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure T uBe (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.
机译:这项研究的目的是使用先前测量的PT直径和工作条件,开发一种束位置线性模型(BPLM)来预测压力T uBe(PT)径向蠕变。燃料通道中有十二个管束,并且对于每个管束,使用诸如快速中子注量和管束冷却剂温度等因变量建立了线性模型。使用减法聚类方法选择训练数据集。 BPLM由韩国Wolsung核电厂的2号,3号和4号机组的49个实测通道中的80%组成的39个通道的数据用于开发BPLM。其余10个通道的数据用于测试开发的BPLM。 BPLM通过最大似然估计方法进行了优化。使用从单元2、3和4收集的操作数据验证了开发的预测PT径向蠕变的BPLM。生成了BPLM的两个误差成分,即认知误差和偶然误差。直径蠕变预测和两个误差分量将用于在相应的有效满功率天数处生成区域超功率跳闸设定点。还生成了均方根(RMS)误差,并将其与当前预测方法的误差进行比较。发现RMS误差小于以前的误差。

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