Neutron flux flattening in PWRs using neural networks in fuel management

The Hopfield network is studied from the standpoint of taking it as a computational model in optimizing the fuel management of pressurized water reactors (PWRs). In this paper, the flattening of the neutron flux is considered as the objective function. By this consideration, the power peaking inside the reactor core is also minimized. Regarding the local minimum problem of Hopfield network, the simulated annealing method is applied to improve the Hopfield solution. The method is applied to Bushehr Nuclear Power Plant (PWR design) and the result is compared with the core configuration purposed by the designer.

[1]  W. B. Terney,et al.  The Design of Reload Cores Using Optimal Control Theory , 1982 .

[2]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[3]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[4]  M. H. Hu.  Anisotropic modelling of tube bundle flow during steam generator wet layup , 1982 .

[5]  Alan H. Robinson,et al.  Determination of optimum fuel loadings in pressurized water reactors using dynamic programming , 1973 .

[6]  Mengkang Peng,et al.  An Investigation into the Improvement of Local Minima of the Hopfield Network , 1996, Neural Networks.

[7]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[8]  T. O. Sauar,et al.  APPLICATION OF LINEAR PROGRAMMING TO IN-CORE FUEL MANAGEMENT OPTIMIZATION IN LIGHT WATER REACTORS. , 1971 .

[9]  D. R. Vondy,et al.  EXTERMINATOR-2: A FORTRAN IV CODE FOR SOLVING MULTIGROUP NEUTRON DIFFUSION EQUATIONS IN TWO DIMENSIONS. , 1967 .

[10]  Tetsuya Higuchi,et al.  Cost coefficient control method for solving optimization problems on Hopfield-type neural networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[11]  Kwang Y. Lee,et al.  Economic load dispatch for piecewise quadratic cost function using Hopfield neural network , 1993 .

[12]  Tsutomu Hoshino,et al.  In-Core Fuel Management Optimization by Heuristic Learning Technique , 1972 .

[13]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[14]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[15]  Hiroshi Motoda,et al.  Optimization of Refueling Schedule for Light-Water Reactors , 1973 .

[16]  Alfred F. Rohach,et al.  Perturbation theory in nuclear fuel management optimization , 1982 .

[17]  Soon Heung Chang,et al.  PRESSURIZED WATER-REACTOR CORE PARAMETER PREDICTION USING AN ARTIFICIAL NEURAL NETWORK , 1993 .

[18]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.