Advanced methods for the design of foundations for offshore wind energy structures

海上风能结构基础设计的先进方法由于全球温室气体排放的很大一部分来自能源，继续吸收可再生能源至关重要。海上风能是英国能源供应的重要组成部分。这促使人们探索新的工程技术，以继续提高海上风能的商业可行性。该项目属于EPSRC风电主题。这项研究将集中于海上风力涡轮机的岩土元素，重点是基础的行为。从选址到资产保护，在海上风力发电项目生命周期的所有阶段，都有应用先进的计算程序和数据驱动的方法的空间。拟议的研究将侧重于开发这些用于预测行为和基础设计的方法。这项研究可能会集中在单桩基础上，这是海上风力涡轮机最常见的基础类型。这项研究的目标受众将是实践者，研究目的是开发新的、健壮的分析技术，以改进海上风能结构基础的设计。这些技术的目标将是快速，同时接近计算昂贵的高保真模型的精度。这将使设计人员能够进行更准确的参数研究和选择，从而创建更优化、更具成本效益的设计。三维有限元分析通常用于高保真的单层土-结构相互作用建模。这在计算上是昂贵的，限制了它在参数研究和可选操作中的使用。本研究将探索快速建立模型的方法，接近三维有限元分析的精度。这可以通过为三维有限元分析创建代理机器学习模型来实现。训练数据可以从三维有限元分析中产生，捕捉到有关地基行为的相关信息。机器学习代理模型可以根据这些数据进行训练，目的是做出快速、准确的预测。以往的研究已经将这种方法应用于基础宏观单元刚度的预测，并将其应用于水轮机上部结构模型的基础支撑条件。作为EPSRC资助的风能和海洋能源系统和结构中心博士培训的一部分，该学生目前在课程开始时坐在授课模块中，因此进行的研究可能与本说明中概述的初步目标不同。

Advanced methods for the design of foundations for offshore wind energy structuresWith a large proportion of global greenhouse gas emissions coming from energy, the continued uptake of renewable energy sources is vital. Offshore wind forms an important component of the UK's energy supply. This motivates the exploration of novel engineering techniques to continue to increase the commercial viability of offshore wind. This project falls within the EPSRC wind power theme. The research will focus on geotechnical elements of offshore wind turbines, focussing on the behaviour of foundations.There is scope for the application of advanced computational procedures and data-driven approaches in all stages of an offshore wind project's lifecycle, from site selection to asset protection. The proposed research will focus on the development of these methods for the prediction of behaviour and design of foundations. The research will likely focus on monopile foundations, which form the most common foundation type for offshore wind turbines. The target audience for this research would be practitioners, with a research aim of developing novel, robust analysis techniques that can improve the design of foundations of offshore wind energy structures. The objective of these techniques will be to be rapid, whilst approaching the accuracy of computationally expensive high-fidelity models. This will enable more accurate parametric study and optioneering from designers, creating more optimal, cost-effective designs.Three-dimensional finite element analysis is typically employed for high fidelity modelling of soil-structure interaction of monopiles. This is computationally expensive, limiting its use for parametric study and optioneering. The research will explore methods to create models which are rapid, approaching the accuracy of three-dimensional finite element analysis. This can be achieved by creating surrogate machine learning models for the three-dimensional finite element analysis. Training data can be generated from the three-dimensional finite element analysis, capturing relevant information about the foundation's behaviour. Machine learning surrogate models can be trained on this data, with the aim of making rapid, accurate predictions. Previous research has applied this approach to make predictions of the foundations macro-element stiffness, which would be applied as a support condition at the base of models of the turbine super-structure. The student is currently sitting taught modules at the start of the course as part of the EPSRC funded Wind and Marine Energy Systems and Structures centre for doctoral training, so the research carried out may differ from the preliminary aims outlined in this description.