FindA University Ltd Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
FindA University Ltd Featured PhD Programmes

Floating Offshore Wind Turbines: development of a machine-learning augmented, physics-based design and optimisation framework for innovative floating support platforms


Department of Naval Architecture, Ocean & Marine Engineering

About the Project

Qualification type: PhD
Location: Glasgow, UK
Funding for: UK and EU only
Funding amount: The funding covers UK or EU student tuition fees and stipend (~£18,000 per year) in line with University rates.
Hours: Full Time
Start Date and Duration: October 2020, for 3 years.
Application closing date: 16 August 2020. Prompt application is advised, as this position is only available until a suitable candidate is found.
Keywords: FOWT, floating wind, offshore wind, renewable energy, offshore renewable energy, dynamics
Project Title: Floating Offshore Wind Turbines: development of a machine-learning augmented, physics-based model for the optimisation of innovative floating support platforms
Overview
Floating offshore wind turbines (FOWT) have the potential to access large untapped wind resources, but at the moment their cost is still quite high. The current FOWT configurations are heavily based on configurations developed for the oil & gas industry, being these the first cautious attempts and therefore focusing on being conservative rather than optimised – leaving large margins for improvement.
The need for alternative, innovative concepts have been highlighted by a number of leading researchers in the field, but either a wider range of constraints and objective functions are considered, but no alternative design framework is proposed, or the proposed design frameworks are limited to considering only some disciplines, or the parametric description of the support platform is not flexible enough to explore alternative design spaces.
The main aim of this proposed PhD topic is to develop an unbiased, prime-principle based multidisciplinary design and analysis framework for floating offshore wind turbine support structures, capable of accommodating multidisciplinary constraints and objectives, in order to identify innovative configurations, potentially leading to the definition of alternative cheaper, safer, more sustainable support platforms.
The objectives are:
- Systematic review of Multi-Disciplinary Design, Analysis, and Optimisation techniques (MDAO), of floating offshore wind turbine modelling and analysis approaches, of advanced optimisation techniques.
- Development of a prime-principle based, unbiased MDAO framework, flexible enough to generate and analyse unconventional configurations, and to accommodate requirements and constraints from a variety of disciplines. This would include: 1) definition of an innovative support structure parametric model, allowing the definition of unconventional shapes, 2) analytical analysis and definition of the objectives and constraints, as function of the design vector formulate in the previous step, 3) implementation of the numerical models (AHSE coupled model of dynamics, techno-economic assessment) necessary to quantify the objectives and constraints, 4) identify the numerical models/steps with the highest computational costs, and substitute them with machine-learning-based surrogate models, capable of approximating with the necessary accuracy the outputs of the computationally expensive models, 5) identification of the most suitable optimisation techniques, considering the optimisation problem defined in the previous steps
- Demonstrate the potential of the MDAO framework developed through an industrially-relevant case study, potentially discovering innovative platform configurations. A benchmark FOWT system will be chosen – and the MDAO framework will be utilised to design, analyse and optimise a support structure for the same wind turbines, operating in the same environmental conditions.
Name of supervisor(s)
Dr Maurizio Collu (primary) - https://www.strath.ac.uk/staff/collumauriziodr/
Dr Andrea Coraddu (secondary) - https://www.strath.ac.uk/staff/coradduandreadr/
Eligibility Criteria
Applicants should have UK or EU nationality.
Applicants should have a distinction pass at Master’s level in naval architecture/ocean engineering/mechanical engineering or a related subject, or first class BEng/BSc Honours degree, or equivalent, in naval architecture/ocean engineering/mechanical engineering or in a related subject.
Applicants must be available to commence academic studies in the UK on October 2020 or slightly after.
Experience with aero-hydro-servo-elastic coupled model of dynamics applied to offshore wind turbines (e.g. FAST by NREL and/or SIMO-RIFLEX by DNVGL), and hydrodynamics frequency analysis codes (WADAM, WAMIT, NEMOH) will be considered a distinct advantage.
Proficiency in a scientific programming language (MATLAB / python) is required.
How to apply
Applicants should send their application directly to Dr Maurizio Collu:
Applications should include:
- Cover Letter
- CV with two referees
- Degree transcripts and certificates
Contact
If you wish to discuss any details of the project informally, please contact Dr Maurizio Collu, e-mail:

Funding Notes

The funding covers UK or EU student tuition fees and stipend (~£18,000 per year) in line with University rates.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to University of Strathclyde will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully



Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.



FindAPhD. Copyright 2005-2020
All rights reserved.