Development of machine learning approaches to geotechnical design of marine renewable energy foundations

开发海洋可再生能源基础岩土工程设计的机器学习方法

• 批准号: 2611858
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2611858
• 关键词: Development; machine; learning; approaches; geotechnical

This project will develop an optimisation tool that allows for geotechnical and foundation design considerations to be incorporated into windfarm layout optimisation, along with the wind and energy generation conditions that are typically focused on in literature. After developing a Neural Network based automated design tool that rapidly emulates cutting edge single monopile design techniques, optimisation and/or genetic programming will be used to carry out whole site design based on various design constraints. The project is directly applicable to the ongoing efforts to transition to renewable energy.The automated pile design methodology will be validated and tested using laboratory tests carried out with the geotechnical centrifuge and/or numerical modelling. The windfarm layout optimisation methodology will be demonstrated using site data from windfarm developments.The project will take advantage of the extensive geotechnical laboratory facilities alongside maritime and machine learning strengths at Southampton.

该项目将开发一种优化工具，该工具允许将岩土工程和基础设计考虑因素纳入风电场布局优化中，沿着文献中通常关注的风力和能源发电条件。在开发了一个基于神经网络的自动化设计工具，该工具可以快速模拟最先进的单芯片设计技术，优化和/或遗传编程将用于根据各种设计约束进行整个现场设计。该项目直接适用于正在进行的向可再生能源过渡的努力。自动桩设计方法将通过土工离心机和/或数值模拟进行实验室测试进行验证和测试。风电场布局优化方法将使用风电场开发的现场数据进行演示。该项目将利用南安普顿广泛的岩土实验室设施以及海事和机器学习优势。