(A) study on the optimal fuel loading pattern design in pressurized water reactors using the artificial neural network and the fuzzy rule based system = 인공신경회로망과 퍼지 규칙기반시스템을 이용한 가압경수로의 최적 노심 재장전 모형의 설계에 관한 연구

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In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economic aspects. Therefore the general problem of incore fuel management for a PWR consists of determining the fuel reloading policy for each cycle that minimize unit energy cost under the constraints imposed on various core parameters, e.g., a local power peaking factor and an assembly burnup. This is equivalent that a cycle length is maximized for a given energy cost under the various constraints. Existing optimization methods do not ensure the global optimum solution because of the essential limitation of their searching algorithms. They only find near optimal solutions. To solve this limitation, a hybrid artificial neural network system is developed for the optimal fuel loading pattern design using a fuzzy rule based system and an artificial neural networks. This system finds the patterns that $P_{\max}$ is lower than the predetermined value and $K_{eff}$ is larger than the reference value. The back-propagation networks are developed to predict PWR core parameters. Reference PWR is an 121-assembly typical PWR. The local power peaking factor and the effective multiplication factor at BOC condition are predicted. To obtain target values of these two parameters, the QCC code are used. Using this code, 1000 training patterns are obtained, randomly. Two networks are constructed, one for $P_{\max}$ and another for $K_{eff}$ Both of two networks have 21 input layer neurons, 18 output layer neurons, and 120 and 393 hidden layer neurons, respectively. A new learning algorithm is proposed. This is called the advanced adaptive learning algorithm. The weight change step size of this algorithm is optimally varied inversely proportional to the average difference between an actual output value and an ideal target value. This algorithm greatly enhances the convergence speed of a BPN. In case of $P_{\max}$ prediction, 98\% of the untrained patterns are predicted wi...
Chang, Soon-Heungresearcher장순흥researcher
한국과학기술원 : 원자력공학과,
Issue Date
68213/325007 / 000885117

학위논문(박사) - 한국과학기술원 : 원자력공학과, 1993.8, [ xvi, 147 p. ]


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