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An Improved Inverse Analysis Model for Fuel Loading Pattern Optimization

An improved inverse analysis model for fuel loading pattern (LP) optimization is proposed to reproduce a desired in-core power distribution from available fuel assemblies based on the inverse analysis of diffusion equations. In the present approach, the inverse analysis is simplified as a fixed-source problem rather than an eigenvalue problem in a previous work. By using the one-to-one correspondence between the power distribution and reactivity distribution, a target reactivity distribution is solved from a desired power distribution through the inverse analysis. The reactivity of available assemblies is evaluated using a modified effective fast-group model with a higher order correction of leakage effect. A candidate LP is determined by individually matching the reactivity of available assemblies to the target in-core reactivity distribution using a stable matching algorithm. The process of predicting the reactivity of available assemblies and matching them to the target reactivity distribution is repeated until the convergence condition is satisfied. Validation of the model has been confirmed with a reference PWR core. The model is expected to be applicable to a problem of multicycle loading pattern optimization.