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Launching into tomorrow: NRIC guides new era of research and deployment
In June 2025, the Department of Energy announced the Reactor Pilot Program, an authorization pathway that allowed reactor developers to partner with the DOE to get first-of-a-kind (FOAK) reactors built and tested. Soon after, the DOE rolled out a complementary Fuel Line Pilot Program, which aimed to fast-track fuel projects. In all, 20 projects were accepted into the new programs.
Atul A. Karve, Paul J. Turinsky
Nuclear Technology | Volume 131 | Number 1 | July 2000 | Pages 48-68
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT00-A3104
Articles are hosted by Taylor and Francis Online.
As part of the continuing development of the boiling water reactor in-core fuel management optimization code FORMOSA-B, the fidelity of the core simulator has been improved and a control rod pattern (CRP) sampling capability has been added. The robustness of the core simulator is first demonstrated by benchmarking against core load-follow depletion predictions of both SIMULATE-3 and MICROBURN-B2 codes. The CRP sampling capability, based on heuristic rules, is next successfully tested on a fixed fuel loading pattern (LP) to yield a feasible CRP that removes the thermal margin and critical flow constraint violations. Its performance in facilitating a spectral shift flow operation is also demonstrated, and then its significant influence on the cost of thermal margin is presented. Finally, the heuristic CRP sampling capability is coupled with the stochastic LP optimization capability in FORMOSA-B - based on simulated annealing (SA) - to solve the combined CRP-LP optimization problem. Effectiveness of the sampling in improving the efficiency of the SA adaptive algorithm is shown by comparing the results to those obtained with the sampling turned off (i.e., only LP optimization is carried out for the fixed reference CRP). The results presented clearly indicate the successful implementation of the CRP sampling algorithm and demonstrate FORMOSA-B's enhanced optimization features, which facilitate the code's usage for broader optimization studies.