一种利用正午报告数据进行装饰优化的灰盒模型方法

IF 0.6 Q4 ENGINEERING, MARINE
Robert H. Zwart, Jordi Bogaard, A. Kana
{"title":"一种利用正午报告数据进行装饰优化的灰盒模型方法","authors":"Robert H. Zwart, Jordi Bogaard, A. Kana","doi":"10.3233/isp-220009","DOIUrl":null,"url":null,"abstract":"Trim optimization improves the energy efficiency of ships, thus reducing operational costs and emissions; however, trim tables are only available for a limited number of ships. There is thus a desire to develop additional, more accurate trim tables without the need for expensive model testing. The objective of this research was to develop a method to decrease fuel consumption by trim optimization, by a dynamic shaft power estimation model based on available operational data. A method that uses noon report data and a grey-box modelling approach is proposed. The grey box model consists of a multi-layer feedforward neural network to estimate the required shaft power, using operational parameters and an initial estimate of the required shaft power. A case study is presented for a modern chemical tanker and sea trials have been conducted to validate the results. The method provides correct trim advice for full load conditions; however, the magnitude of the effect is smaller compared to sea trial results. The model is able to estimate the required power with an average accuracy of over 6% for a random subset of the noon report data. Due to challenges inherent to noon reports as a data source, the actual effect of trim and speed have a bigger magnitude than the extracted trend.","PeriodicalId":45800,"journal":{"name":"International Shipbuilding Progress","volume":"1 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Grey-box model approach using noon report data for trim optimization\",\"authors\":\"Robert H. Zwart, Jordi Bogaard, A. Kana\",\"doi\":\"10.3233/isp-220009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Trim optimization improves the energy efficiency of ships, thus reducing operational costs and emissions; however, trim tables are only available for a limited number of ships. There is thus a desire to develop additional, more accurate trim tables without the need for expensive model testing. The objective of this research was to develop a method to decrease fuel consumption by trim optimization, by a dynamic shaft power estimation model based on available operational data. A method that uses noon report data and a grey-box modelling approach is proposed. The grey box model consists of a multi-layer feedforward neural network to estimate the required shaft power, using operational parameters and an initial estimate of the required shaft power. A case study is presented for a modern chemical tanker and sea trials have been conducted to validate the results. The method provides correct trim advice for full load conditions; however, the magnitude of the effect is smaller compared to sea trial results. The model is able to estimate the required power with an average accuracy of over 6% for a random subset of the noon report data. Due to challenges inherent to noon reports as a data source, the actual effect of trim and speed have a bigger magnitude than the extracted trend.\",\"PeriodicalId\":45800,\"journal\":{\"name\":\"International Shipbuilding Progress\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Shipbuilding Progress\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/isp-220009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Shipbuilding Progress","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/isp-220009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
引用次数: 0

摘要

内饰优化提高了船舶的能源效率,从而降低了运营成本和排放;然而,装饰表只适用于有限数量的船只。因此,需要开发额外的,更精确的修剪表,而不需要昂贵的模型测试。本研究的目的是开发一种基于可用运行数据的动态轴功率估计模型,通过内饰优化来降低燃油消耗的方法。提出了一种利用正午报告数据和灰盒建模的方法。灰盒模型由多层前馈神经网络组成,利用运行参数和所需轴功率的初始估计来估计所需轴功率。以一艘现代化学品船为例进行了研究,并进行了海试以验证结果。该方法为满载条件提供正确的修剪建议;然而,与海上试验结果相比,影响的幅度较小。对于中午报告数据的随机子集,该模型能够以超过6%的平均精度估计所需的功率。由于正午报告作为数据源所固有的挑战,修剪和速度的实际影响比提取的趋势更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Grey-box model approach using noon report data for trim optimization
Trim optimization improves the energy efficiency of ships, thus reducing operational costs and emissions; however, trim tables are only available for a limited number of ships. There is thus a desire to develop additional, more accurate trim tables without the need for expensive model testing. The objective of this research was to develop a method to decrease fuel consumption by trim optimization, by a dynamic shaft power estimation model based on available operational data. A method that uses noon report data and a grey-box modelling approach is proposed. The grey box model consists of a multi-layer feedforward neural network to estimate the required shaft power, using operational parameters and an initial estimate of the required shaft power. A case study is presented for a modern chemical tanker and sea trials have been conducted to validate the results. The method provides correct trim advice for full load conditions; however, the magnitude of the effect is smaller compared to sea trial results. The model is able to estimate the required power with an average accuracy of over 6% for a random subset of the noon report data. Due to challenges inherent to noon reports as a data source, the actual effect of trim and speed have a bigger magnitude than the extracted trend.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
8
期刊介绍: The journal International Shipbuilding Progress was founded in 1954. Each year four issues appear (in April, July, September and December). Publications submitted to ISP should describe scientific work of high international standards, advancing subjects related to the field of Marine Technology, such as: conceptual design structural design hydromechanics and dynamics maritime engineering production of all types of ships production of all other objects intended for marine use shipping science and all directly related subjects offshore engineering in relation to the marine environment ocean engineering subjects in relation to the marine environment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信