Employing Nodal Generalized Perturbation Theory for the Minimization of Feed Enrichment during Pressurized Water Reactor In-Core Nuclear Fuel Management Optimization

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Abstract
The computer code FORMOSA-P (Fuel Optimization for Reloads Multiple Objectives by Simulated Annealing—PWR) has been developed to address pressurized water reactor (PWR) in-core nuclear fuel management optimization. Until recently, the optimization objectives available to the user included minimization of relative power peaking throughout the cycle, maximization of the end-of-cycle reactivity, and maximization of region-average discharge burnup. In addition, during an optimization, various core attributes (including the preceding objectives) can be optionally activated as constraints via penalty functions or to directly reject sampled loading patterns that violate established design limits. The underlying theoretical framework that enables the accurate and efficient calculation of objective and constraint values within the FORMOSA-P code is its higher order, nodal generalized perturbation theory (GPT) neutronics model. The utility of the FORMOSA-P code has been extended to include a traditionally out-of-core decision variable, namely, the fresh (i.e., feed) reload fuel enrichment. This is accomplished by formulating the feed enrichment as a GPT variable that can be adjusted concurrently with changes in the core loading pattern to enforce a target cycle length. This provides a reload designer with the capability to minimize feed enrichment during an in-core optimization while enforcing all other constraints (e.g., power peaking limit, cycle energy requirement, degree of eighth-core power tilt, discharge burnup limit, and moderator temperature coefficient limit).