A Hybrid Digital Twin Framework for Predictive Maintenance and Dynamic Performance Optimisation of Rail Vehicle Bogies

About the Project

This PhD project focuses on the development of a next‑generation hybrid digital twin framework for rail vehicle bogies, addressing major challenges in safety, ride quality, reliability, and whole‑life maintenance costs within the rail industry. Rail bogies are critical vehicle subsystems that directly influence passenger comfort and operational safety. Despite their importance, current maintenance strategies remain largely periodic and reactive, which limits the ability to detect early‑stage degradation and optimise lifecycle performance. This research aims to move beyond these limitations by developing a real‑time, predictive digital twin that continuously represents the physical state of a rail bogie during operation.

The project will combine high‑fidelity physics‑based multibody dynamics modelling with data‑driven analytics and machine‑learning techniques. Dynamic models will be developed to capture key bogie behaviours such as suspension response and wheel–rail interaction. These models will be integrated with real‑world sensor data, including vibration, acceleration, temperature, and strain measurements, using data‑assimilation techniques such as filtering and statistical updating. Machine‑learning methods will then be applied to detect anomalies, diagnose emerging faults, and predict the remaining useful life of critical components. Consideration will also be given to scalability and computational efficiency to ensure that the developed framework is suitable for deployment across operational rail fleets and supports the transition towards intelligent, data‑driven railway systems.

Entry Requirements

• Qualifications: A good honours degree (minimum 2:1) in Mechanical, Civil, Railway, or a closely related Engineering discipline. A relevant MSc is desirable.
• Skills & Experience: Strong analytical and mathematical skills with an interest in dynamic systems, modelling, and data analysis. Experience with numerical simulation or programming (e.g. MATLAB or Python) is advantageous but not essential.
• Additional Knowledge: Knowledge of railway engineering, condition monitoring, or data‑driven analysis would be beneficial but is not required.

Application Details

• A motivational email as to why you wish to apply for the scholarship, stating which project you are applying for.
• Full CV
• Provide copies of transcripts and certificates of all relevant academic and/or any professional qualifications.
• Provide references from two individuals – please contact your referees and ask them to send your reference directly to gs.pgradmissions@hud.ac.uk from their email address. (references can be submitted late but must be received by 17th May 2026)
• Proof of eligibility – e.g. scan of passport photo page
• International applicants must be able to provide an IELTS 6.5 (with no element below 6) *or equivalent English Language certificate or proof of study in the UK within the past 2 years, for their application to be considered for shortlisting.

Please email gs.pgradmissions@hud.ac.uk with queries regarding eligibility and submitting documents. Informal enquiries about individual projects should be directed to the lead supervisor listed for each project.

Funding Notes

3 years full-time research covering tuition fees and a tax-free bursary (stipend) starting at £21,805 for 2026/27.