Multiscale Failure Analysis and AI-Assisted Prediction of High-Voltage Dynamic Subsea Cables for Floating Offshore Wind

This PhD offers the opportunity to investigate real failure mechanisms in high-voltage dynamic subsea cables used in floating offshore wind. Working with ORE Catapult and an international cable manufacturer, the student will access industrial cable samples, inspection evidence, operational data and full-scale testing.<br /> <br /> The project will determine how cyclic bending, contact, fretting, abrasion and marine exposure interact across cable layers and length scales. <br /> <br /> The student will:<br /> - Build a field-informed failure dataset and taxonomy from inspection records, operating histories and damaged samples;<br /> - Reproduce priority mechanisms using multiscale mechanical, tribological and environmental testing;<br /> - Develop and calibrate finite-element and reduced-order models of bending, contact, slip and local stress;<br /> - Combine mechanistic features and experimental variables using interpretable machine learning and uncertainty analysis; <br /> - Validate predictions against withheld industrial cases and translate the findings into design maps, test protocols and engineering guidance.<br /> <br /> The project provides distinctive interdisciplinary training across materials science, offshore engineering, computational modelling and interpretable AI.<br />

<p style="margin-bottom:11px"><strong>Background</strong><br /> Floating offshore wind relies on dynamic subsea cables that must survive millions of loading cycles while operating in aggressive marine environments. Cable failures can lead to long outages, expensive offshore intervention and premature replacement. Predicting failure remains difficult because bending, contact, fretting, abrasion and environmental degradation interact across multiple cable layers and length scales. Field observations, laboratory tests and system-level models are often considered separately. This project will connect these evidence sources to identify the mechanisms and operating conditions that govern damage progression and to support more representative qualification and life-assessment methods.<br />  </p> <p><strong>Methodology</strong><br /> Industrial inspection records, operating histories and damaged cable samples will be used to identify priority failure mechanisms. These will be reproduced using SEM/EDX, 3D surface metrology, nanoindentation, fretting and abrasion testing, simulated marine exposure and cyclic loading. Finite-element and reduced-order models will simulate bending, layer contact, slip and local stress, calibrated against laboratory and full-scale ORE Catapult data. Physics-based features and experimental variables will then inform interpretable machine-learning models with cross-validation and uncertainty quantification. Predictions will be tested on withheld industrial cases and manufacturer samples before translation into design maps and test guidance.</p> <p><br /> <strong>Skills Required</strong><br /> Applicants should have, or expect to obtain, a first-class or upper second-class degree (2:1), or equivalent, in mechanical engineering, or a closely related discipline. A relevant master's degree is desirable but not essential. We welcome candidates interested in combining hands-on experiments with modelling and data analysis. Experience in materials characterisation, mechanical testing, tribology, finite-element analysis, Python, data science or machine learning would be advantageous, but specialist training will be provided. Strong analytical, communication and teamwork skills are essential.<br />  </p> <p><strong>Industry/external partners</strong><br /> ORE Catapult will provide access to full-scale cable testing, inspection and operational data, specialist facilities and offshore engineering expertise. Dr David Hoyle and Dr Ping Lu will contribute technical direction, mentoring and industrial validation. A confidential international high-voltage cable manufacturer will provide representative cable samples, design context and technical input. Together, the partners will help select industrially relevant failure cases, support model calibration and validation, assess AI-assisted predictions, and translate the research into practical recommendations for cable design, qualification and testing.</p> <p> </p>

<p>To apply for this project you will need to <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">make a formal application for research degree study through the University's website</a>. You will need to create a login ID with a username and PIN. </p> <ul> <li>For <strong>‘Application type’</strong> please select <strong>‘Research Degrees – Research Postgraduate’. </strong></li> <li>The <strong>admission year</strong> for this project is<strong> 2027/28 Academic Year.</strong></li> <li>You will need to select your <strong>‘Planned Course of Study’</strong> from a drop-down menu. For this project, scroll down and select <strong>‘EPSRC DLA (Engineering, Biological Sciences, Medicine)’ </strong></li> <li>The project start date for this project is <strong>01/10/2027</strong>, please use this as your <strong>Proposed Start Date of Research</strong>. </li> <li>Please state clearly in the research information section that the research degree you wish to be considered for is <strong>‘Multiscale Failure Analysis and AI-Assisted Prediction of High-Voltage Dynamic Subsea Cables for Floating Offshore Wind’</strong> as well as <a href="https://eps.leeds.ac.uk/mechanical-engineering/staff/14131/dr-mingwen-bai">Dr Mingwen Bai </a>as your proposed supervisor.</li> </ul> <p><strong>Please state clearly in the finance section that the funding you are applying for is EPSRC Doctoral Landscape Award 2027/28: Mechanical Engineering.</strong></p> <p>You will be required to provide a personal statement which outlines your interest in the project you are applying for, why you have chosen it and how your skills map onto the requirements of the project. </p> <p><strong>Please note that you must provide the following documents in support of your application by the closing date of 18 November 2026 or at the point you submit your application:</strong></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date including grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</p>

Applicants to research degree programmes in the School of Mechanical Engineering should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the supervisor or Admissions Team prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>A highly competitive EPSRC Doctoral Landscape Award (DLA) providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate (currently £21,805 in academic session 2026/27) for 3.5 years and rising annually, in line with standard UKRI rates. Training and support will also be provided.</p> <p>This opportunity is open to UK applicants only. </p> <p><br /> <strong>Competition funded  </strong></p> <ul> <li>"Competition funded" means that we advertise a larger number of projects than available awards. Not all projects advertised for this funding opportunity will receive funding. The selection panel will consider all applications received across the full range of advertised projects and select the awardee/s based on the strength of their application. </li> </ul> <p><br /> <strong>Eligibility Criteria </strong></p> <ul> <li>Applicants must be eligible to pay fees at the Home (UK) rate.</li> </ul> <p class="MsoNoSpacing"><br /> <strong>Other Conditions</strong></p> <ul> <li class="MsoNoSpacing">Candidates who have previously been awarded a PhD or are currently registered on a PhD are excluded from applying. Those who were previously studying for a PhD but did not complete may be considered. </li> <li>Awards must be taken up by 1st October 2027.</li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship.</li> </ul> <p><br /> If you are unsure whether you are eligible for UK fees/funding, please see our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page.</a></p> <p> </p>

<p>For further information please contact the PGR Admissions Engineering and Physical Sciences team on this email address: <a href="http://phd@engineering.leeds.ac.uk/">phd@engineering.leeds.ac.uk</a></p> <p>If you are interested in this project or have any questions, please contact the project supervisor <a href="https://eps.leeds.ac.uk/mechanical-engineering/staff/14131/dr-mingwen-bai">Dr Mingwen Bai </a>on this address: <a href="mailto:M.Bai2@leeds.ac.uk">M.Bai2@leeds.ac.uk</a></p>