多目标优化在流体机械中的应用研究综述
A Review of the Application of Multi-Objective Optimization Method in Fluid Machinery
DOI: 10.12677/SE.2023.136007, PDF,    科研立项经费支持
作者: 陶粤婷, 王 伟, 吴玉庭, 马重芳：北京工业大学能源与动力工程学院，北京；传热强化与过程节能教育部重点实验室，北京；传热与能源利用北京市重点实验室，北京
关键词: 流体机械；多目标优化；优化算法；近似模型；Fluid Machinery； Multi-Objective Optimization； Optimization Algorithm； Approximation Model
摘要: 流体机械综合性能提升有利于提高能源系统能效，引入多目标优化方法对流体机械展开性能优化研究，可以进一步改善流体机械整体性能。因此针对多目标优化方法在流体机械性能优化中的应用展开了综述。首先总结了多种流体机械多目标性能优化现状，其次简要介绍了多目标优化的基础概念，并概述了常用的几种优化算法及其在流体机械中的相关应用。流体机械多目标优化的基础是性能预测，性能预测方法的优劣对于多目标优化效果有着决定性影响。对流体机械性能预测方法展开了分析讨论，重点探讨了近似模型技术，详细介绍了最常用的几种近似模型并总结其在流体机械优化中的应用。最后提出了目前优化中存在的问题，为日后流体机械多目标优化研究提供参考。
Abstract: The comprehensive performance improvement of fluid machinery is beneficial to improving the efficiency of the energy system. The introduction of multi-objective optimization method can further improve the overall performance of fluid machinery. Therefore, the application of multiobjective optimization method in fluid machinery performance optimization was summarized. Firstly, the present situation of multi-objective performance optimization of various fluid machinery was re-viewed. Secondly, the basic concept of multi-objective optimization was briefly introduced, and several common optimization algorithms and their related applications in fluid machinery were summarized. Performance prediction is the basis of multi-objective optimization of fluid machinery, and the performance prediction methods have a decisive influence on the effect of multi- objective optimization. Therefore, the prediction methods of fluid machinery were analyzed and discussed, with emphasis on the approximation model methods, the most common types of approximation model methods were introduced in detail and their applications in fluid machinery optimization were summarized. Finally, the existing problems in the optimization were put forward to provide reference for the future multi-objective optimization research of fluid machinery.
文章引用：陶粤婷, 王伟, 吴玉庭, 马重芳. 多目标优化在流体机械中的应用研究综述[J]. 可持续能源, 2023, 13(6): 69-80. https://doi.org/10.12677/SE.2023.136007

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