{"title":"基于粒子群优化的加氢进料泵性能预测与优化--最小二乘支持向量回归代用模型","authors":"Yanpi Lin, Liang Li, Shunyin Yang, Xiaoguang Chen, Xiaojun Li, Zuchao Zhu","doi":"10.1080/19942060.2024.2315985","DOIUrl":null,"url":null,"abstract":"Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. I...","PeriodicalId":50524,"journal":{"name":"Engineering Applications of Computational Fluid Mechanics","volume":"6 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance prediction and optimization of hydrogenation feed pump based on particle swarm optimization – least squares support vector regression surrogate model\",\"authors\":\"Yanpi Lin, Liang Li, Shunyin Yang, Xiaoguang Chen, Xiaojun Li, Zuchao Zhu\",\"doi\":\"10.1080/19942060.2024.2315985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. I...\",\"PeriodicalId\":50524,\"journal\":{\"name\":\"Engineering Applications of Computational Fluid Mechanics\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Computational Fluid Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19942060.2024.2315985\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Computational Fluid Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19942060.2024.2315985","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Performance prediction and optimization of hydrogenation feed pump based on particle swarm optimization – least squares support vector regression surrogate model
Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. I...
期刊介绍:
The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.