Boiling Water Reactor In-Core Fuel Management Through Parallel Simulated Annealing in FORMOSA-B.
Boiling Water Reactor In-Core Fuel Management Through Parallel Simulated Annealing in FORMOSA-B.

Author:

Publisher:

Published: 2004

Total Pages:

ISBN-13:

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A commercial nuclear power plant with a boiling water reactor will utilize between 368 and 800+ individual fuel assemblies to generate steam for 18 to 24 months between refueling outages. The composition and reactivity of each fuel assembly will vary due to variations in initial enrichment, burnable poison loading and irradiation conditions in the core. These variations pose a challenge to the engineers who must design subsequent reloads because only one quarter to one half of the fuel will be replaced at a time. One of the challenges is to determine the optimum layout of the fuel within the core in order to get the highest value from the fuel without violating any safety or operational limits. The FORMOSA-B program was developed to automatically find a family of optimum loading patterns by combining a robust, accurate 3-D core simulator with a simulated annealing loading pattern search. Other features have been added to allow the program to rapidly compute shutdown margins and optimize control rod programming through the application of heuristic rules. One drawback of the FORMOSA-B program is that long run-times, sometimes exceeding a week, are required to generate and evaluate the large numbers of solutions required by the simulated annealing algorithm. The rising popularity and availability of parallel computing and computational clusters provides a possible solution to the problem of long run-times. To this end, a parallel simulated annealing capability has been developed for the FORMOSA-B program. The parallel simulated annealing driver utilizes standardized Message Passing Interface routines to divide the individual Markov search chains of the simulated annealing algorithm among a large number of processors. By evaluating multiple loading patterns concurrently, run times are significantly reduced. In testing with a 368-assembly BWR/4 model, parallel speedup factors exceeding 32 were observed with 48 processors. Parallel efficiencies are calculated to be in the.

Multi-cycle Boiling Water Reactor Fuel Cycle Optimization
Multi-cycle Boiling Water Reactor Fuel Cycle Optimization

Author: Keith Everette Ottinger

Publisher:

Published: 2014

Total Pages: 140

ISBN-13:

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A multi-cycle nuclear fuel cycle optimization code, BWROPT (Boiling Water Reactor OPTimization), has been developed. BWROPT uses the Parallel Simulated Annealing (PSA) algorithm to solve the coupled out-of-core and in-core optimization problems. There are two depletion methods used for the in-core optimization: the Haling depletion and a Control Rod Pattern (CRP) search. The result of this optimization is the optimum new fuel inventory and the core loading pattern for the first cycle considered in the optimization. Several changes were made to the optimization algorithm with respect to other nuclear fuel cycle optimization codes that use PSA. Instead of using constant sampling probabilities for the solution perturbation types throughout the optimization, as is usually done, the sampling probabilities can be varied to get a better solution and/or decrease runtime. Also, the new fuel types available for use can be sorted into an array based on any parameter so that each parameter can be incremented or decremented. In addition several evaluations were performed to test the CRP search option. Using the variable sampling probabilities was found to produce slightly better results in less time than the standard method of having constant sampling probabilities. Performing ordered and random sampling of the new fuel types using the new fuel type array was found to yield slightly better solutions on average than random sampling alone, but with a somewhat higher runtime. Using variable length Markov chains for optimizations in which a CRP search is performed for the first cycle and the Haling depletion is used for the remaining cycles was found to increase CPU utilization by 33%. Starting the CRP search with the CRP determined for the previous solution was found to be better than starting the CRP search with all of the rods fully withdrawn. Using the CRP search in an optimization was slow and produced inferior results compared to using the Haling depletion, indicating the need for more work in this area.