Optimization of in-core nuclear fuel management in a pressurized water reactor Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/4b29b947r

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  • Fuel loading patterns which have a minimum power peak are economically desirable to allow power reactors to operate at the highest possible power density and to minimize the possibility of fuel failure. A computer code called SHUFLE was developed for pressurized water reactors which shuffles the fuel in search of the lowest possible power peaking factor. An iterative approach is used in this search routine. A radial power distribution is calculated from which the program logic Selects a movement of fuel elements in an attempt to lower the radial power peak. Another power calculation is made and the process repeated until a predetermined convergence is reached. The logic by which the code decides the fuel movement is presented, along with the criteria for accepting or rejecting the move after a power calculation of the new loading pattern is made. A 1.5 group course mesh diffusion theory method was used to obtain the power distribution for each SHUFLE iteration. Convergence to a final loading pattern varies from about 10 to 40 shuffling iterations depending on the initial loading presented to the code. Since the typical computer running time for a one-quarter core power distribution with this 1.5 group method is only one to a few seconds, depending on the loading, convergence to a good loading pattern takes on the order of one minute on a Univac 1108. The low computer cost plus ease of operation should make this code of considerable use in determining loading patterns with minimum power peaking for any given set of fuel elements. The program also has burnup capability which can be used to check power peaking throughout core life. A parametric analysis study of fuel cycle costs for a PWR is also presented. Cost parameters analyzed were variation in the cost of yellow cake, enrichment, money, fabrication, and reprocessing plus changes in burnup, load factors, power densities, and the effect of forced early discharge. Figures are presented to indicate total fuel costs as a function of burnup for these cost parameters. Linear relationships for minimum cost and optimum burnup are presented for each parameter.
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