{"title":"CFD 驱动的船舶修整优化：集成 ANN 以开发用户友好型软件工具","authors":"Matija Vasilev, Milan Kalajdžić, Ines Ivković","doi":"10.3390/jmse12081265","DOIUrl":null,"url":null,"abstract":"This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CFD-Powered Ship Trim Optimization: Integrating ANN for User-Friendly Software Tool Development\",\"authors\":\"Matija Vasilev, Milan Kalajdžić, Ines Ivković\",\"doi\":\"10.3390/jmse12081265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.\",\"PeriodicalId\":16168,\"journal\":{\"name\":\"Journal of Marine Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Marine Science and Engineering\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/jmse12081265\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Marine Science and Engineering","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/jmse12081265","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
CFD-Powered Ship Trim Optimization: Integrating ANN for User-Friendly Software Tool Development
This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.
期刊介绍：
Journal of Marine Science and Engineering (JMSE; ISSN 2077-1312) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to marine science and engineering. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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