Article(id=1263557426490500003, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185166237, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185166237, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185166237, creator=13701087609, updateTime=1779185166237, updator=13701087609, issue=Issue{id=1263557421033767765, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='1', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1779185164936, creator=13701087609, updateTime=1779185226134, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263557677813252309, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263557677813252310, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=165, endPage=176, ext={EN=ArticleExt(id=1263557428235330481, articleId=1263557426490500003, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=A study on a main engine power prediction model for a VLCC, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

Improving ship energy efficiency and reducing greenhouse gas emissions are major research priorities in the maritime industry. Accurate prediction of main engine power is fundamental to enhancing vessel energy efficiency. Using historical operational data collected from a Very Large Crude Carrier (VLCC), this study integrated and cleaned meteorological data to construct training and test datasets. Three models for main-engine power estimation are investigated and compared:a mechanistic model （SNNM), a non-mechanistic model based on Random Forest （RF), and a semi-mechanistic RF-based model. Simulation results indicate that while the mechanistic SNNM model can meet application requirements under specific engineering conditions, but R² coefficient is relatively low. In contrast, both the non-mechanistic model based on RF and the semi-mechanistic RF-based model demonstrated excellent predictive accuracy for both main engine shaft rotational speed and power, with R² values exceeding 0. 98.

, correspAuthors=Guoxiang DONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yingbin CHEN, Guoxiang DONG, Sheng JI, Yanfei ZHANG), CN=ArticleExt(id=1263557431460750305, articleId=1263557426490500003, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于VLCC船舶的主机功耗预估模型研究, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

提高船舶的能效水平，减少温室气体排放是目前的研究热点。对船舶主机功率进行准确的预测是提高船舶的能效水平的基础。基于一艘超大型油轮（VLCC）船舶采集的历史营运数据，对其进行气象数据融合清洗并构建训练集与测试集。分别研究机理模型SNNM、非机理模型RF、半机理模型RF等3种船舶主机功耗预估模型的性能表现。仿真结果表明：机理模型SNNM在工程一定条件下是满足应用要求的，但R²系数表现并不佳，非机理模型RF和半机理模型RF对主机轴转速和轴功率的预测精度十分优异，其R²系数均大于0.98。

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陈映彬（1998—），男，硕士，研究方向为船舶节能减排。

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陈映彬（1998—），男，硕士，研究方向为船舶节能减排。

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