Article(id=1221507475384226074, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221507468635586855, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212292, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670860800000, receivedDateStr=2022-12-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769159676642, onlineDateStr=2026-01-23, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769159676642, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769159676642, creator=13701087609, updateTime=1769159676642, updator=13701087609, issue=Issue{id=1221507468635586855, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='6', pageStart='1', pageEnd='172', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769159675034, creator=13701087609, updateTime=1769166411362, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221535722931216843, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221507468635586855, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221535722931216844, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221507468635586855, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=127, endPage=134, ext={EN=ArticleExt(id=1221507475669438760, articleId=1221507475384226074, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Design and performance prediction of supercritical carbon dioxide centrifugal compressor, columnId=1221507471064092771, journalTitle=Thermal Power Generation, columnName=Equipments research on S-CO₂ cycle power generation system, runingTitle=null, highlight=null, articleAbstract=

Centrifugal compressor is one of the key components in supercritical carbon dioxide (S-CO₂) cycle system, which plays a decisive role in the efficiency and stable operation of the system. Different from the traditional air compressor, the unique physical properties of S-CO₂ working medium make the internal flow field of the compressor more complex. The loss model established based on the physical characteristics of air also needs to be modified specifically to meet the performance prediction requirements of S-CO₂ centrifugal compressor. Therefore, numerical simulation is needed to investigate the internal flow field characteristics of the compressor, so as to improve the compressor performance prediction method accordingly. Firstly, one-dimensional aerodynamic parameters of the compressor were designed, and a three-dimensional model was established based on the one-dimensional design parameters to analyze the characteristics of the internal flow field of the compressor. It was found that the shunt blade had a great influence on the internal flow field, and changes in the internal flow field of the impeller under varying working conditions would also cause changes in the outlet flow Angle. Based on this, The sliding factor and the calculated blade number of the compressor under off-design conditions were corrected, and the surface friction coefficient was improved to predict the performance of the compressor under off-design conditions. The numerical simulation results show that the prediction error of the improved model is significantly reduced, and the average efficiency error decreases from 2.03% to 0.16% under off-design conditions.

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离心压缩机是超临界二氧化碳（S-CO₂）循环系统中的关键部件之一，对系统的效率和稳定运行起决定性作用。区别于传统空气工质压缩机，S-CO₂工质的独特物性使得压缩机内部流场更为复杂；基于空气物性特点建立的损失模型也需要做针对性的修正以满足S-CO₂离心压缩机的性能预测要求，因此需要数值模拟研究探明压缩机内部流场特性，以此对压缩机性能预测方法做相应的改进。首先开展压缩机一维气动参数设计，并在一维设计参数的基础上建立三维模型，分析压缩机内部流场特点，发现分流叶片对内部流场有较大影响，同时变工况下叶轮内部流场发生改变也会引起出口气流角的变化，基于此，对压缩机非设计工况下滑移因子和计算叶片数进行修正，同时改进表面摩擦系数以预测压缩机的非设计工况性能。数值模拟结果表明，改进后模型的预测误差显著降低，非设计工况下平均效率误差从2.03%下降到0.16%。

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孙恩慧（1991），男，博士，讲师，主要研究方向为先进发电技术，ehsun@ncepu.edu.cn。

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孙恩慧（1991），男，博士，讲师，主要研究方向为先进发电技术，ehsun@ncepu.edu.cn。

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