Fuel management optimization using genetic algorithms and expert knowledge
Journal Article
·
· Nuclear Science and Engineering
OSTI ID:379815
- Pennsylvania State Univ., University Park, PA (United States)
The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.
- OSTI ID:
- 379815
- Journal Information:
- Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering Journal Issue: 1 Vol. 124; ISSN NSENAO; ISSN 0029-5639
- Country of Publication:
- United States
- Language:
- English
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