A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers

被引:40
|
作者
Stewart, Ryan H. [1 ]
Palmer, Todd S. [1 ]
DuPont, Bryony [1 ]
机构
[1] Oregon State Univ, 1500 SW Jefferson St, Corvallis, OR 97331 USA
关键词
Multi-objective problem; Optimization; Nuclear engineering; INSPIRED EVOLUTIONARY ALGORITHM; FUEL LOADING PATTERN; OBJECTIVE REDUCTION; GENETIC ALGORITHMS; TABU SEARCH; SUPPORT; DESIGN;
D O I
10.1016/j.pnucene.2021.103830
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Problems in nuclear engineering-such as reactor core design-involve a multitude of design variables including fuel or assembly configurations; all of which require careful consideration when constrained by a set of objectives such as fuel temperature or assembly power density. Reactor design is one facet of nuclear engineering, where many nuclear engineers often face large multi-objective problems to solve. These types of problems can be solved by relying upon experts to aid in reducing the design space required for multi-objective optimization, however, computational optimization algorithms have been used to generate optimal solutions with reproducibility and quantitative evidence for designs. We present a review of multi-objective optimization literature including an introduction to optimization theory, commonly used multi-objective optimization algorithms, and current applications in nuclear science and engineering. From this review, researchers will glean an understanding of multi-objective optimization algorithms that are currently available, and gain a fundamental understanding of how to apply these techniques to a wide variety of problems in the fields of nuclear science and engineering.
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收藏
页数:16
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