The complete design process for a new nuclear power plant concept is costly, long, complicated, and the work is generally split between several specialized groups. These design groups separately do their best to design the portion of the reactor that falls in their expertise according to the design criteria before passing the design to the subsequent design group. Ultimately, the work of each design group is combined, with significant iteration between groups striving to facilitate the integration of each of the heavily interdependent systems. Such complex interaction between experts leads to three significant problems: (1) the issues associated with knowledge management, (2) the lack of design optimization, and (3) the failure to discover the hidden interdependencies between different design parameters that may exist. Some prior work has been accomplished in both developing common frame of reference (CFR) support systems to aid in the design process and applying optimization to nuclear system design.The purpose of this work is to use multi-objective optimization to address the second and third problems above on a small subset of reactor design scenarios. Multi-objective optimization generates several design optima in the form of a Pareto front, which portrays the optimal trade-off between design objectives. As a major part of this work, a system design optimization tool is created, namely the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). The OPTIONS tool is initially applied to several individual nuclear systems: the power conversion system (PCS) of the Integral, Inherently Safe Light Water Reactor (IÂ²S-LWR), the Kalina cycle being proposed as the PCS for a LWR, the PERCS (or Passive Endothermic Reaction Cooling System), and the core loop of the Zion plant. Initial sensitivity analysis work and the application of the Non-dominated Sorting Particle Swarm Optimization (NSPSO) method provides a Pareto front of design optima for the PCS of the IÂ²S-LWR, while bringing to light some hidden pressure interdependencies for generating steam using a flash drum. A desire to try many new PCS configurations leads to the development of an original multi-objective optimization method, namely the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA). With this method, the OPTIONS tool provides a novel and improved Pareto front with additional optimal PCS configurations. Then, the simpler NSGA method is used to optimize the Kalina cycle, the PERCS, and the Zion core loop, providing each problem with improved designs and important objective trade-off information. Finally, the OPTIONS tool uses the MI-NSGA method to optimize the integration of three systems (Zion core loop, PERCS, and Rankine cycle PCS) while increasing efficiency, decreasing costs, and improving performance. In addition, the tool is outfitted to receive user preference input to improve the convergence of the optimization to a Pareto front.
College and Department
Ira A. Fulton College of Engineering and Technology; Chemical Engineering
BYU ScholarsArchive Citation
Wilding, Paul Richard, "The Development of a Multi-Objective Optimization and Preference Tool to Improve the Design Process of Nuclear Power Plant Systems" (2019). Theses and Dissertations. 7515.
multi-objective optimization, pareto optimization, genetic algorithm, nuclear power plant, common frame of reference