Multi-Objective Optimisation of Floating Offshore Windfarms

减少我们对化石燃料依赖的努力导致了可再生能源部门的快速增长，特别是在海上风力发电方面。近岸静态风力涡轮机（OWT）的效率已得到广泛证明; 2019年第一季度约有10%的发电量来自海上风电，英国政府希望到2030年将这一比例提高到30%。最近，人们的注意力已经转移到浮式海上风力涡轮机（FOWT）的设计上，该风力涡轮机可以放置在更深的水中以利用更强的风力条件。该项目将探索多目标技术，优化FOWT阵列的设计，以实现最大的效益，尽可能减少成本和尽可能减少对环境的影响。静态OWT的优化已被广泛研究。FOWT阵列优化提出了一个更加复杂的问题，因为它需要使用一个更复杂的模型，能够纳入涡轮机的运动。该项目是计算机科学和数学之间的跨学科合作，适合任何背景的候选人。

The drive to reduce our dependency on fossil fuels has led to rapid growth in the renewable energy sector, particularly with respect to offshore wind power. The efficacy of near-shore static wind turbines (OWTs) has been extensively demonstrated; approximately 10% of power generated in Q1 of 2019 came from offshore wind, and the UK Government seeks to increase this to 30% by 2030.Evolutionary computation has been widely applied to the design of OWT arrays. Recently, attention has moved to the design of floating offshore wind turbines (FOWTs) which can be placed in deeper water to take advantage of stronger wind conditions. This project will explore multi-objective techniques for optimising the design of FOWT arrays such that maximal benefit is achieved for the minimum possible cost and at the minimum possible environmental impact.The optimisation of static OWTs has been extensively studied. FOWT array optimisation poses a substantially more complex problem as it requires the use of a more complex model capable of incorporating the motion of the turbines. This project is an interdisciplinary collaboration between Computer Science and Mathematics, and would suit a candidate from either background.