{"title":"基于计算流体力学和机器学习方法的多级离心泵结构优化","authors":"Jiantao Zhao, J. Pei, J. Yuan, Wenjie Wang","doi":"10.1093/jcde/qwad045","DOIUrl":null,"url":null,"abstract":"\n To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Structural optimization of multistage centrifugal pump via computational fluid dynamics and machine learning method\",\"authors\":\"Jiantao Zhao, J. Pei, J. Yuan, Wenjie Wang\",\"doi\":\"10.1093/jcde/qwad045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.\",\"PeriodicalId\":48611,\"journal\":{\"name\":\"Journal of Computational Design and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2023-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Design and Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1093/jcde/qwad045\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jcde/qwad045","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Structural optimization of multistage centrifugal pump via computational fluid dynamics and machine learning method
To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.