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Application of Multiple Objective Particle Swarm Optimisation in the Design of Damaged Offshore Mooring Systems

机译:多目标粒子群优化在损坏近岸系泊系统设计中的应用

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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's, 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 esign 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:海上碳氢化合物行业在更多的敌对环境中运作,随着更多边缘场在经济上可行。这意味着将需要更多浮动生产系统和经济的系泊系统。随着利用边缘领域的这种增加,需要重新使用船只和系泊。浮动生产系统,如FPSO,需要在极端的事件和极端损害条件下生存。当一个系泊线损坏时,其余的必须足以避免完全失败,并且仍然保护诸如立管之类的关键组件。本文探讨了进化优化技术,即多重客观粒子群优化,对损坏的系泊型肌肉和分析。对海上客观函数的评估是计算昂贵的,因为它需要使用复杂的模拟。当客观函数评估的数量很大,就像具有进化方法的情况一样,即使是快速计算机也不是不合需要的长时间才能完成工作。因此,需要具有很大效率的强大优化算法来最小化总运行的数量。

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