Funding providers: UKRI and Swansea University

Subject areas: Computational Engineering/ Machine Learning

Project start date:

• 1 July 2023 (Enrolment open from mid–June)

Project supervisors: 

• Professor Oubay Hassan (o.hassan@swansea.ac.uk)
• Professor Rubén Sevilla

Aligned programme of study: PhD in Civil Engineering

Mode of study: Full-time

Project description:

Exposure to hazardous substances from manufacturing, storage and transport poses significant risk to public health and to the environment. Planning control of hazardous substances has been one of the main strategies at different national agencies (e.g. Environmental Agency) to understand the associated risks during the planning phase as well as the operational stage of both new and existing facilities. Apart from extensive networks of physical sensors being deployed island-wide to monitor air quality and pollutants, numerical models of pollutant dispersion for planning and operational purposes have been used. The current industrial numerical approaches are often fast to provide predictions; they are, however, much less accurate due to inherent assumptions and simplifications embedded in those models. There exists a class of higher fidelity methodologies for the prediction of pollutant dispersion using computational fluid dynamic (CFD) approaches. Unfortunately, the CFD-based models are resource intensive and time-consuming to execute; thus, rendering them impractical for industrial usage. The current proposal aims at addressing this gap by developing a fast CFD-based approach for prediction of pollutant dispersion.

Computational methods for predicting complex unsteady flows are computationally demanding, while full-field experimental methods are very expensive. This project is at the forefront of the emerging field of machine learning and data-driven computational modelling. The project aims at developing unique new models to predict fluid mechanic characteristics by integrating advances in high order CFD techniques for simulations of urban flows with accuracy and robustness The aim is to build a surrogate model for wind wake prediction using high fidelity CFD data - thus taking into account the effects of the urban built environment (i.e. buildings and greenery). This development will form a fundamental building block for the hybrid platform on pollutant dispersion monitoring and response.

The resulting learning-based models will vastly reduce the computational cost of running CFD simulations. This reduction in cost will expand the potential applications of CFD into new areas such as generative design, digital twins, life-cycle forecasting for engineering structures and inverse problems.

Candidates must normally hold an undergraduate degree at 2.1 level (or Non-UK equivalent as defined by Swansea University) in Engineering or similar relevant science discipline. See ‘country specific information for European and international applicants (available at the bottom of the page)'.

English Language requirements: If applicable – IELTS 6.5 overall (with at least 5.5 in each individual component) or Swansea recognised equivalent. Details on the Swansea University English Language entry policy can be found here.

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations. 

NB: If you are holding a non-UK degree, please see Swansea University degree comparisons to find out if you meet the eligibility.

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in.

This scholarship covers the full cost of UK tuition fees and an annual stipend of £17,668.

Additional research expenses will also be available.

To apply, please complete your application online with the following information:

1. Course choice – please select Civil Engineering / PhD / Full-time / 3 Year / JulyIn the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.
2. Start year – please select 2023
3. Funding (page 8) –

• ‘Are you funding your studies yourself?’ – please select No
• ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS290 - PhD Civil Eng'

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

We encourage you to complete the following to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University:

• Equality, Diversity and Inclusion (EDI) Monitoring Form (online form)

As part of your online application, you MUST upload the following documents (please do not send these via email):

• CV
• Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
• A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
• Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
• Evidence of meeting English Language requirement (if applicable).
• Copy of UK resident visa (if applicable)

Informal enquiries are welcome, please contact Professor Oubay Hassan (o.hassan@swansea.ac.uk).

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.