多变环境要素耦合作用下的船舶能效动态智能优化算法研究

Intelligent optimization of ship energy efficiency is an important part of promoting energy conservation and emission reduction as well as ship intellectualization in shipping industry. In this research, the large bulk carrier is selected as the research object, and the dynamic intelligent optimization algorithm of ship energy efficiency under the coupling effect of multiple varying environmental factors is studied. The data of navigational environment, navigational state and ship energy consumption are obtained, in order to achieve the analyses and prediction of ship energy efficiency state based on the multi-source information. According to the theory of ship propulsion, the coupling mechanism between multiple environmental factors and ship propulsion is studied by a combination of empirical formula and numerical simulation. The mathematical model and simulation model of ship energy efficiency under the coupling effect of multiple environmental factors are established. The rolling optimization of the parameters of the energy efficiency model under different navigational conditions is realized by on-line self-learning parameter identification method, so as to improve the accuracy and effectiveness of the model. Based on the real-time perception and prediction of multi-source information, an online prediction model and a dynamic optimization model of ship energy efficiency based on real-time multi-source information are established. The intelligent autonomous decision making and on-line rolling optimization of ship energy efficiency are realized by designing the dynamic intelligent optimization algorithm based on model predictive control and swarm intelligence. This research can realize real-time analyses, evaluation and intelligent decision-making of ship energy efficiency, and lay a theoretical and technical foundation for the intelligent management of ship energy efficiency, contributing to the development of green and intelligent ships.

船舶能效智能优化是促进航运业节能减排与船舶智能化发展的重要一环。本项目以大型散货船为对象，研究多变环境要素耦合作用下的船舶能效动态智能优化算法。通过获取通航环境、船舶航行状态及能耗等数据，开展基于多源信息的船舶能效状态数据挖掘分析与预测。根据船舶推进理论，采用理论计算和数值仿真相结合的方法，研究多环境要素与船舶推进系统之间的耦合作用机理，建立多环境要素耦合作用下的船舶能效机理模型与仿真模型，通过在线自学习参数辨识方法实现不同航行条件下能效模型参数的滚动优化，从而提高模型的准确性与有效性。基于多源信息的实时感知与预测，建立基于实时信息的船舶能效在线预测模型和动态优化模型，通过开发基于模型预测控制和群智能的能效动态智能优化算法，实现船舶能效的智能自主决策与在线滚动优化。本研究可实现船舶能效的实时分析、评估与智能决策，将为船舶营运能效的智能管理奠定理论与技术基础，服务于船舶的绿色化与智能化发展。

船舶能效智能优化是促进航运业节能减排与船舶智能化发展的有效措施之一，也是落实国家“双碳”发展战略的迫切需要。本项目以提升船舶能效水平为目标，开展了多变环境要素耦合作用下的船舶能效动态智能优化算法研究。通过获取通航环境、航行姿态及船舶能耗等数据，开展了通航环境与船舶能效数据特征挖掘分析研究，揭示了不同航行状态下船舶能效主要影响因素及其影响关系，建立了航行工况辨识模型与能效状态评估模型，实现了基于多源信息的船舶航行状态智能识别，以及船舶动力系统能耗特性分析和能效状态评估；基于航行环境信息和船舶推进系统运行参数数据，分析了不同环境条件下推进系统运行特性及其能量转换关系，研究了不同通航环境和航行状态下的船舶阻力特性、螺旋桨特性及推进系统能量传递特性，分析了不同航行状态下的多环境要素与船舶能效的动态响应关系，揭示了多环境要素与船舶能效耦合作用机理；建立了考虑多因素的船舶能效机理模型，实现了不同航行条件下船舶能效水平的有效表征。开展了基于人工智能的船舶能效智能预测模型研究，提出了基于智能算法参数优化的船舶能效智能预测方法；通过建立考虑多变环境要素的船舶能效动态联合优化模型，构建了多变环境要素耦合作用下的船舶能效动态智能优化策略与优化算法，可实现多变环境要素耦合作用下船舶能效的智能决策与动态联合优化。相应研究成果可为船舶能效的智能管理奠定理论与技术基础，对促进船舶的绿色化与智能化发展具有重要意义。

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