Bayesian inference along Markov Chain Monte Carlo approach for PWR core loading pattern optimization

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Despite remarkable progress in optimization procedures, inherent complexities in nuclear reactor structure and strong interdependence among the fundamental indices namely, economic, neutronic, thermo-hydraulic and environmental effects make it necessary to evaluate the most efficient arrangement of a reactor core. In this paper a reactor core reloading technique based on Bayesian inference along Markov Chain Monte Carlo, BIMCMC, is addressed in the context of obtaining an optimal configuration of fuel assemblies in reactor cores. The Markov Chain Monte Carlo with Metropolis–Hastings algorithm has been applied for sampling variable and its acceptance. The proposed algorithm can be used for in-core fuel management optimization problems in pressurized water reactors. Considerable work has been expended for loading pattern optimization, but no preferred approach has yet emerged. To evaluate the proposed technique, increasing the effective multiplication factor Keff of a WWER-1000 core along flattening power with keeping power peaking factor below a specific limit as a first test case and flattening of power as a second test case are considered as objective functions; although other variables such as burn up and cycle length can also be taken into account. The results, convergence rate and reliability of the new method are compared to published data resulting from particle swarm optimization and genetic algorithm; the outcome is quite promising and demonstrating the potential of the technique very well for optimization applications in the nuclear engineering field.