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Hull geometry optimisation of wave energy converters: On the choice of the optimisation algorithm and the geometry definition

机译:波能转换器的船体几何优化:关于优化算法的选择与几何定义

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摘要

It is key in the development of wave energy systems to aim at designing economically competitive solutions that enable maximal annual energy production. Previous studies identify the Wave Energy Converter (WEC) structure, i.e. the hull, to have one of the largest cost reduction potentials. Due to this potential, geometry optimisation of WECs has been previously considered, however, most of these studies have been limited by the simplicity of the employed geometrical shapes and the lack of accurate cost models. It is, therefore, important to include an adaptable geometry definition capable of generating diverse WEC shapes, and to account for other factors that can have an effect on costs. These considerations result in a more challenging optimisation problem, and a more complex objective function. The goal of this study is to address the challenge of finding a suitable and efficient optimisation method for WEC geometry design. In this paper, different geometry definitions, such as using simple shapes or B-spline surfaces, and different meta-heuristic optimisation algorithms, such as genetic algorithms or particle swarm optimisation are applied to this problem to find the most suitable choices. Results show an improvement in final objective function values of up to 224% when using an adaptable geometry definition and up to 11% when employing the most suitable optimisation algorithm compared to previous results. In conclusion, the choice of the different elements of the optimisation formulation have a large impact on the quality of the optimisation results and should be based on preliminary studies as presented here.
机译:它是波能系统开发的关键,以设计在经济上具有最大年度能源生产的经济上竞争解决方案。以前的研究识别波能转换器(WEC)结构,即船体,具有最大的成本降低电位之一。由于这种潜力,以前考虑了WECS的几何优化,然而,这些研究中的大多数受采用的几何形状的简单性和缺乏准确的成本模型受到限制。因此,重要的是包括能够产生各种WEC形状的可适应性的几何定义,并考虑可以对成本产生影响的其他因素。这些考虑因素导致更具挑战性的优化问题,以及更复杂的客观函数。本研究的目标是解决寻找WEC几何设计合适有效优化方法的挑战。在本文中,不同的几何定义,例如使用简单的形状或B样条表面以及不同的遗传算法或粒子群优化的不同的元启发式优化算法,以找到最合适的选择。结果显示使用适应性几何定义和最多11%的最终目标函数值的最终目标函数值的提高,与以前的结果相比,使用最合适的优化算法。总之,优化配方的不同元素的选择对优化结果的质量产生了很大的影响,并且应基于此处提出的初步研究。

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