The use of multi-objective optimization to improve the design process of nuclear power plant systems
- Brigham Young Univ., Provo, UT (United States)
Multi-objective optimization is proposed to address the problem of design optimality in the nuclear power plant design process. This is done via the creation/development of the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). This work details applications of multi-objective optimization methods (in Python 3) to two different nuclear system design problems: the flash-Rankine power conversion system (PCS) of the I2S-LWR and the Passive Endothermic Reaction Cooling System (PERCS) attached to a standard PWR primary system (in RELAP5). The PCS design problem is first solved for three configurations using a specific particle swarm optimization method, achieving a thermodynamic efficiency of 34.81%. A new method, the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA), is created and then used, solving a PCS superstructure of 22 configurations and achieving an efficiency of 35.63%. Finally, the OPTIONS tool is used to optimize the PERCS, improving cost, core temperature control, and cooling longevity.
- Research Organization:
- Brigham Young Univ., Provo, UT (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC07-05ID14517; NRC-HQ-84-16-G-0008
- OSTI ID:
- 1848137
- Alternate ID(s):
- OSTI ID: 1579476
- Journal Information:
- Annals of Nuclear Energy, Vol. 137, Issue C; ISSN 0306-4549
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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