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DNB limit estimation using an adaptive fuzzy inference system

机译：DNB使用自适应模糊推理系统限制估计

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The onset of nucleate boiling is characterized by extremely high heat transfer rates. However, if the fuel rod is operated at a high enough power density, the heat transfer mechanism becomes film boiling with severely reduced heat transfer ability, which is called Departure from Nucleate Boiling (DNB). In this work, the DNB is predicted by an adaptive fuzzy inference system using the measured signals of the average temperature, pressure, and coolant flowrate of a reactor core. An adaptive fuzzy inference system is a fuzzy inference system equipped with a training algorithm. The training method of the adaptive fuzzy inference system is accomplished by two steps: the combined genetic and least-squares algorithms (first step), and the combined back-propagation and least-squares algorithms (second step). The proposed method was verified by using the nuclear and thermal data of the Yonggwang 3 and 4 nuclear power plants. Even though the rule number of this algorithm is small (4 rules), the estimate is accurate. Therefore, this algorithm can provide good information for nuclear power plant operation and diagnosis by predicting the DNB each time step.
机译：核心沸腾的发作的特征在于极高的传热速率。然而，如果燃料棒以足够高的功率密度操作，则传热机制变为薄膜沸腾，传热能力严重降低，这被称为核心沸腾（DNB）。在这项工作中，使用反应器芯的平均温度，压力和冷却剂流量的测量信号来预测DNB通过自适应模糊推理系统预测。自适应模糊推理系统是一种具有培训算法的模糊推理系统。自适应模糊推理系统的训练方法由两个步骤完成：组合的遗传和最小二乘算法（第一步骤）和组合的背部传播和最小二乘算法（第二步骤）。通过使用永王3和4核电厂的核和热数据来验证所提出的方法。即使该算法的规则次数很小（4规则），估计值是准确的。因此，通过每次步骤预测DNB，该算法可以提供核电厂操作和诊断的良好信息。

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