@article{hays_turinsky_2014, title={STOCHASTIC OPTIMIZATION FOR NUCLEAR FACILITY DEPLOYMENT SCENARIOS USING VISION}, volume={186}, ISSN={["1943-7471"]}, DOI={10.13182/nt13-68}, abstractNote={Abstract The process of transitioning from the current once-through nuclear fuel cycle to a hypothetical closed fuel cycle necessarily introduces a much greater degree of supply feedback and complexity. When considering such advanced technologies, it is necessary to consider when and how fuel cycle facilities can be deployed in order to avoid resource conflicts while maximizing certain stakeholder values. A multiobjective optimization capability was developed around the VISION nuclear fuel cycle simulation code to allow for the automated determination of optimum deployment scenarios and objective trade-off surfaces for dynamic fuel cycle transition scenarios. A parallel simulated annealing optimization framework with modular objective function definitions is utilized to maximize computational power and flexibility. Three sample objective functions representing a range of economic and sustainability goals are presented, as well as representative optimization results demonstrating both robust convergence toward a set of optimum deployment configurations and a consistent set of trade-off surfaces.}, number={1}, journal={NUCLEAR TECHNOLOGY}, author={Hays, Ross and Turinsky, Paul}, year={2014}, month={Apr}, pages={76–89} }
 @article{hays_turinsky_2011, title={BWR in-core fuel management optimization using parallel simulated annealing in FORMOSA-B}, volume={53}, ISSN={["0149-1970"]}, DOI={10.1016/j.pnucene.2010.09.002}, abstractNote={The process of finding optimized fuel reload patterns for boiling water reactors is complicated by a number of factors including the large number of fuel assemblies involved, the three-dimensional neutronic and thermal-hydraulic variations, and the interplay of coolant flow rate with control rod programming. The FORMOSA-B code was developed to provide an automated method for finding fuel loading patterns, control rod programs and coolant flow rate schedules to minimize certain quantitative metrics of core performance while satisfying given operational constraints. One drawback of this code has been the long runtimes required for a complete cycle optimization on a desktop workstation (oftentimes several days or more). To address this shortcoming, a parallel simulated annealing algorithm has been added to the FORMOSA-B code, so that the runtimes may be greatly reduced by using a multiprocessor computer cluster. Tests of the algorithm on a sample problem indicate that it is capable of parallel efficiencies exceeding 80% when using four processors.}, number={6}, journal={PROGRESS IN NUCLEAR ENERGY}, author={Hays, Ross and Turinsky, Paul}, year={2011}, month={Aug}, pages={600–606} }