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Application of multiple objective particle swarm optimisation in the design of damaged offshore mooring systems

机译:多目标粒子群算法在受损海上系泊系统设计中的应用

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摘要

The offshore hydrocarbon industry operates in more hostile environments as more of marginal fields become economically viable. This means that more floating production systems and economical mooring systems will be needed. With this increase in the use of marginal fields goes the need to re-use vessels and moorings. Floating production systems, such as FPSO\u27s, need to survive extreme events and extreme damage conditions. When one mooring line is damaged, the remaining ones must be sufficient to avoid a complete failure and still protect critical components such as the riser. This paper looks into applying an evolutionary optimisation technique, namely multiple objective particle swarm optimisation, to the damaged mooring design and analysis. The evaluation of offshore objective functions is computationally expensive since it requires use of complex simulations. When the number of objective function evaluations is large, as is the case with evolutionary methods, even a fast computer takes undesirably long to complete the job. Hence, a robust optimisation algorithm with great efficiency is required to minimise the number of total runs.
机译:随着更多的边际油田在经济上变得可行,海上油气行业将在更加不利的环境中运转。这意味着将需要更多的浮动生产系统和经济的系泊系统。随着边缘区域使用的增加,需要重新使用船只和系泊设备。浮式生产系统(例如FPSO \ u27s)需要在极端事件和极端破坏条件下生存。当一条系泊缆绳损坏时,其余缆绳必须足够避免完全失败,并仍保护关键组件(例如立管)。本文研究将进化优化技术(即多目标粒子群优化)应用于受损的系泊设计和分析。离岸目标函数的评估在计算上很昂贵,因为它需要使用复杂的模拟。当目标函数评估的数量很大时(如进化方法那样),即使是快速的计算机也要花费不希望的时间来完成这项工作。因此,需要一种高效的鲁棒优化算法来最大程度地减少总运行次数。

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