This project is one of a number that are in competition for funding from the University of Bath URSA competition. Please see the URSA webpage for more information. https://www.bath.ac.uk/campaigns/university-research-studentship-award-ursa/
Project
Harnessing marine renewable energy is part of the future targets and major investment that will accelerate the UK’s path to net zero by 2050. Efficient and accurate modelling of marine renewable energy devices during operation and storm conditions is essential in the design and optimisation as laboratory experiments are expensive and take much longer time to collect limited data. A systematic modelling of the devices under various conditions is important for the assessment of the performance of the devices and for the optimisations.
In the proposed research, a new efficient numerical method and open-source model will be developed to help accelerate the development of marine renewable energy. The traditional Computational Fluid Dynamics (CFD) models solve complex N-S equations on grids and sometimes need weeks or months of computations. This is clearly not feasible in the design and development of marine renewable energy devices. In the proposed research, Data Assimilation and Machine Learning will be used to accelerate the traditional CFD models for the optimisation and survival analysis that is required for improving the performance of marine renewable energy devices. Existing data or data to be generated during the research will be used to train the model to ensure the newly developed model to be able to reproduce the physics of the engineering problems in an efficient manner, within hours or minutes.
A couple of typical wave energy devices, and floating offshore wind systems, such as heaving buoy wave energy devices, and spar type of floating offshore wind system etc will be used as examples for the applications of this new efficient model. Careful validations with existing published experimental data will be carried out before an open-source model is to be developed and released, to help accelerate the development and deployment of marine renewable energy.
Our research group is currently collaborating with machine learning and data science research group in the Department of Computer Science at the University of Bath, and the Applied Computation and Modelling Group at Imperial College London. The proposed research will be expected to work closely with these two research groups during the proposed research.
Candidate Requirements
Applicants should hold, or expect to receive, an undergraduate Masters first class degree or MSc distinction in Engineering, Mathematics, Physics, Natural science or Computer Science (or non-UK equivalent).
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications
Informal enquiries are welcomed and should be directed to Dr Jun Zang - [Email Address Removed]
Formal applications should be made via the University of Bath’s online application form for a PhD in Civil Engineering.
When completing the form, please identify your application as being for the URSA studentship competition in Section 3 Finance (question 2) and quote the project title and lead supervisor’s name in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found on our website.
Funding Eligibility
To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements: UK nationals (living in the UK or EEA/Switzerland), Irish nationals (living in the UK or EEA/Switzerland), those with Indefinite Leave to Remain and EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme). This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.
Equality, Diversity and Inclusion
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Keywords
Civil Engineering; Data Science; Environmental Engineering; Fluid Mechanics; Machine Learning; Marine Engineering; Offshore Engineering
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