Development of machine learning approaches to geotechnical design of marine renewable energy foundations
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Description
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.
| University of Southampton | LEAD_ORG |
| Susan Gourvenec | SUPER_PER |
Subjects by relevance
- Optimisation
- Machine learning
- Projects
- Automation
Extracted key phrases
- Geotechnical design
- Marine renewable energy foundation
- Foundation design consideration
- Windfarm layout optimisation methodology
- Edge single monopile design technique
- Pile design methodology
- Extensive geotechnical laboratory facility
- Site design
- Design tool
- Windfarm development
- Design constraint
- Optimisation tool
- Geotechnical centrifuge
- Energy generation condition
- Machine learning strength