Novel diagnostics to assess the impact of turbulent flow in cardiovascular disease

Cardiovascular disease is the leading cause of death worldwide. Recently, turbulence in blood flow has been linked with cardiovascular disease progression, however the underlying turbulence-related mechanisms are not well understood. This is mostly because it is difficult to measure in patients, and it is time-consuming and expensive to model using computational fluid dynamics (CFD). To understand the mechanisms of turbulence in cardiovascular disease, new and efficient methods need to be developed to enable large-scale studies.

This project will develop new numerical methods to enable large-scale studies into turbulence in cardiovascular flows. In particular, we aim to increase numerical efficiency at both CFD simulation and post-processing stages. At the simulation stage, we will utilise machine learning and multi-fidelity modelling to reduce computational effort. This will involve combining coarse computations; which are fast and cheap, with a small number of high-fidelity turbulence-resolving simulations; which are accurate and expensive. At the post-processing stage, we will utilise methods from time-series analysis to minimise the length of the CFD simulations needed for accurate analysis of turbulence haemodynamics. The method developed in this project will then be applied to a large dataset of cardiovascular disease patients to evaluate turbulence mechanisms on disease progression.

Expected Outcomes

1. Develop an efficient method to enable fast and accurate evaluation of turbulence-related haemodynamics.

2. Apply the method to a large dataset of cardiovascular disease patients to correlate turbulence with disease progression.

3. Work with clinicians to develop novel metrics of key relevance to clinical decision making.

Training Opportunities

The student will benefit from working alongside a multidisciplinary team of clinicians and engineers at the University of Manchester. In particular, the student will have the opportunity to become a visitor at Wythenshawe Hospital, South Manchester, in order to facilitate collaboration with clinicians and cardiovascular imaging specialists to obtain patient data. Depending on students’ interests, there is also potential for an international secondment with our collaborators at the University of Cape Town and Red Cross War Memorial Children's Hospital, South Africa who specialise in paediatric cardiovascular disease. Training can be provided in computational fluid dynamics, machine learning and time-series. This project would suit a student with a strong background in fluid dynamics and experience in/or desire to learn machine learning methods and time-series analysis.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science, mathematics or engineering related discipline.

• Excellence in Fluid Mechanics and Mathematics

• Programming skills in any language

• Ideally, some experience in machine learning and/or statistical analysis methods

• Strong written and verbal communication skills

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).