浮式多用途海上平台整体运动与结构变形预测:平台自动化与控制系统设计背景下的工程方法

IF 4.4 2区 工程技术 Q1 ENGINEERING, OCEAN
Fabrizio Lagasco , Ambra Zuccarino , Alessandro Largo , Giovanni Gambaro , Carlo Ruzzo , Anita Santoro , Felice Arena
{"title":"浮式多用途海上平台整体运动与结构变形预测:平台自动化与控制系统设计背景下的工程方法","authors":"Fabrizio Lagasco ,&nbsp;Ambra Zuccarino ,&nbsp;Alessandro Largo ,&nbsp;Giovanni Gambaro ,&nbsp;Carlo Ruzzo ,&nbsp;Anita Santoro ,&nbsp;Felice Arena","doi":"10.1016/j.apor.2025.104701","DOIUrl":null,"url":null,"abstract":"<div><div>Integrating renewable energy sources with aquaculture systems on floating Multi-purpose Offshore Installations (MOI) offers an innovative and sustainable solution to harness the vast potential of the ocean. Despite several designs have been attempted, the difficulties to prove at harsh environment in real scale remain the main challenge. Whilst the industrial target of the proposed platform design is to enable automated aquaculture production supported by wind, wave and solar energy harvesting technologies in a profitable way benefiting of the available energy resources, as well as of the healthier conditions for fish farming, the technical challenge is focused on the capacity to withstand the harsh environment of the deep waters installation and to guarantee structural integrity during the in-service lifetime. Test before Invest principle requires studying these installations through increasing steps, experiencing prototypes at various scales to investigate about the installation dynamics and capacity to resist the loading conditions over the operative life. The full-scale design, among others, has to consider how to secure insurance coverage for the asset over its operational lifetime, and how to support decisions on extending the asset’s service life, particularly following the replacement of long-lead technological components at the 25-year mark. Designing robust and efficient control and monitoring system based on key structural and deformation data measurements through the operative life may support these achievements, even if practical applications are not available yet. In the context of the design of an innovative MOI developed within “The Blue Growth Farm” (BGF) European Commission (EC) funded H2020 project, the challenge to study and implement a model to predict the floating platform movement and deformation under in-service loading has emerged to be key to define an efficient automation and control system for the installation. This paper describes the engineering process adopted to achieve this objective, considering the novelty of the platform concept and the lack of similar industrial MOI automation and control experience. The proposed methodology focuses on the use of machine learning (ML) techniques and its validation process by exploiting data acquired during an experimental campaign at sea developed on a scaled version (prototype) of the proposed infrastructure. Tests carried out aimed at capturing the complex structure dynamics through data recorded in a wide experimental campaign between May and September 2021 at the Natural Ocean Engineering Laboratory (NOEL) of Reggio Calabria (Italy).</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"161 ","pages":"Article 104701"},"PeriodicalIF":4.4000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Global movement and structural deformation prediction of a floating multi-purpose offshore platform: an engineering approach in the context of the design of the platform automation and control system\",\"authors\":\"Fabrizio Lagasco ,&nbsp;Ambra Zuccarino ,&nbsp;Alessandro Largo ,&nbsp;Giovanni Gambaro ,&nbsp;Carlo Ruzzo ,&nbsp;Anita Santoro ,&nbsp;Felice Arena\",\"doi\":\"10.1016/j.apor.2025.104701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Integrating renewable energy sources with aquaculture systems on floating Multi-purpose Offshore Installations (MOI) offers an innovative and sustainable solution to harness the vast potential of the ocean. Despite several designs have been attempted, the difficulties to prove at harsh environment in real scale remain the main challenge. Whilst the industrial target of the proposed platform design is to enable automated aquaculture production supported by wind, wave and solar energy harvesting technologies in a profitable way benefiting of the available energy resources, as well as of the healthier conditions for fish farming, the technical challenge is focused on the capacity to withstand the harsh environment of the deep waters installation and to guarantee structural integrity during the in-service lifetime. Test before Invest principle requires studying these installations through increasing steps, experiencing prototypes at various scales to investigate about the installation dynamics and capacity to resist the loading conditions over the operative life. The full-scale design, among others, has to consider how to secure insurance coverage for the asset over its operational lifetime, and how to support decisions on extending the asset’s service life, particularly following the replacement of long-lead technological components at the 25-year mark. Designing robust and efficient control and monitoring system based on key structural and deformation data measurements through the operative life may support these achievements, even if practical applications are not available yet. In the context of the design of an innovative MOI developed within “The Blue Growth Farm” (BGF) European Commission (EC) funded H2020 project, the challenge to study and implement a model to predict the floating platform movement and deformation under in-service loading has emerged to be key to define an efficient automation and control system for the installation. This paper describes the engineering process adopted to achieve this objective, considering the novelty of the platform concept and the lack of similar industrial MOI automation and control experience. The proposed methodology focuses on the use of machine learning (ML) techniques and its validation process by exploiting data acquired during an experimental campaign at sea developed on a scaled version (prototype) of the proposed infrastructure. Tests carried out aimed at capturing the complex structure dynamics through data recorded in a wide experimental campaign between May and September 2021 at the Natural Ocean Engineering Laboratory (NOEL) of Reggio Calabria (Italy).</div></div>\",\"PeriodicalId\":8261,\"journal\":{\"name\":\"Applied Ocean Research\",\"volume\":\"161 \",\"pages\":\"Article 104701\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Ocean Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0141118725002871\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, OCEAN\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Ocean Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141118725002871","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 0

摘要

将可再生能源与浮动多用途海上设施(MOI)上的水产养殖系统相结合,为利用海洋的巨大潜力提供了一种创新和可持续的解决方案。尽管已经尝试了几种设计,但在实际恶劣环境中进行验证的困难仍然是主要的挑战。虽然拟议平台设计的工业目标是实现由风能,波浪和太阳能收集技术支持的自动化水产养殖生产,以有利可图的方式受益于可用的能源资源,以及更健康的养鱼条件,但技术挑战集中在承受深水安装的恶劣环境的能力,并保证在使用寿命期间的结构完整性。投资前测试原则要求通过增加步骤来研究这些装置,体验不同规模的原型,以调查装置的动态和在使用寿命期间抵抗负载条件的能力。在全面设计中,必须考虑如何确保资产在其使用寿命期间的保险覆盖,以及如何支持延长资产使用寿命的决策,特别是在25年的时间内更换长期领先的技术部件之后。即使目前还没有实际应用,基于关键的结构和变形数据测量,设计强大而高效的控制和监测系统也可能支持这些成就。在“蓝色增长农场”(BGF)欧盟委员会(EC)资助的H2020项目中开发的创新MOI设计的背景下,研究和实施预测在使用载荷下浮动平台运动和变形的模型的挑战已经成为定义有效自动化和控制系统的关键。考虑到平台概念的新颖性和缺乏类似工业MOI自动化和控制经验,本文描述了实现这一目标所采用的工程过程。拟议的方法侧重于机器学习(ML)技术的使用及其验证过程,方法是利用在拟议基础设施的缩放版本(原型)上开发的海上实验活动中获得的数据。通过2021年5月至9月在意大利雷焦卡拉布里亚自然海洋工程实验室(NOEL)进行的广泛实验活动中记录的数据,进行了旨在捕捉复杂结构动力学的测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global movement and structural deformation prediction of a floating multi-purpose offshore platform: an engineering approach in the context of the design of the platform automation and control system
Integrating renewable energy sources with aquaculture systems on floating Multi-purpose Offshore Installations (MOI) offers an innovative and sustainable solution to harness the vast potential of the ocean. Despite several designs have been attempted, the difficulties to prove at harsh environment in real scale remain the main challenge. Whilst the industrial target of the proposed platform design is to enable automated aquaculture production supported by wind, wave and solar energy harvesting technologies in a profitable way benefiting of the available energy resources, as well as of the healthier conditions for fish farming, the technical challenge is focused on the capacity to withstand the harsh environment of the deep waters installation and to guarantee structural integrity during the in-service lifetime. Test before Invest principle requires studying these installations through increasing steps, experiencing prototypes at various scales to investigate about the installation dynamics and capacity to resist the loading conditions over the operative life. The full-scale design, among others, has to consider how to secure insurance coverage for the asset over its operational lifetime, and how to support decisions on extending the asset’s service life, particularly following the replacement of long-lead technological components at the 25-year mark. Designing robust and efficient control and monitoring system based on key structural and deformation data measurements through the operative life may support these achievements, even if practical applications are not available yet. In the context of the design of an innovative MOI developed within “The Blue Growth Farm” (BGF) European Commission (EC) funded H2020 project, the challenge to study and implement a model to predict the floating platform movement and deformation under in-service loading has emerged to be key to define an efficient automation and control system for the installation. This paper describes the engineering process adopted to achieve this objective, considering the novelty of the platform concept and the lack of similar industrial MOI automation and control experience. The proposed methodology focuses on the use of machine learning (ML) techniques and its validation process by exploiting data acquired during an experimental campaign at sea developed on a scaled version (prototype) of the proposed infrastructure. Tests carried out aimed at capturing the complex structure dynamics through data recorded in a wide experimental campaign between May and September 2021 at the Natural Ocean Engineering Laboratory (NOEL) of Reggio Calabria (Italy).
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
Applied Ocean Research
Applied Ocean Research 地学-工程:大洋
CiteScore
8.70
自引率
7.00%
发文量
316
审稿时长
59 days
期刊介绍: The aim of Applied Ocean Research is to encourage the submission of papers that advance the state of knowledge in a range of topics relevant to ocean engineering.
×
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:604180095
Book学术官方微信