Physics-Based ML & Predictive Digital Twins for Submarines
About the Project
Build a fully physics-based synthetic environment to support the design of nuclear submarine systems, integrating ICME, reliability modelling and AI-assisted digital twins.
- Lead Supervisor: Angelo R. Maligno University of Derby
- Second Supervisor: To be confirmed
- Industry Partner: Partner Sought
- Project Start: TBC
- Target Background: Engineering, materials science, computational mechanics, applied mathematics
- Industrial Funding: In place (Sought)
- Advert Close Date: TBC
- Programme: 4 year Engineering Doctorate (EngD) with industry placement
From empirical testing to certification by analysis.
The proposed project stems from funded European and UKRI-wide proposals in which digital technologies for multiscale design are adopted. The particular task of this study is to start the implementation of a fully physics-based synthetic environment to support the overall design of nuclear submarine systems.
The overall aim is to introduce a predictive digital engineering framework integrating three complementary scientific pillars:
- Integrated Computational Materials Engineering (ICME) for multiscale modelling of materials and manufacturing processes.
- Physics-of-Failure reliability modelling enabling probabilistic fatigue and damage-tolerance assessment.
- AI/ML-assisted digital engineering environments coupling synthetic simulation, structural modelling and predictive digital twins.
This integrated approach will enable earlier reliability assessment, reduced reliance on large-scale physical testing and improved predictive capability for certification-relevant structural behaviour.
Aims and objectives
The ambition is to demonstrate that predictive digital engineering can transform the development of nuclear systems from a predominantly empirical, test-driven process to a simulation-supported certification paradigm. Specific objectives include:
- Reduction in the number of required physical tests in the certification test pyramid.
- Improved accuracy of fatigue life prediction compared with conventional design approaches.
- Validated coupling between operating-conditions simulation, structural modelling and digital twin prediction.
- Demonstration of a predictive digital twin capable of supporting virtual structural assessment at component level.
- Quantified reduction in structural mass while satisfying the same load and reliability requirements.
- Quantified improvement in predicted service life and reduction in premature replacement risk.
Alignment to STAND-UP impact targets
- >50% reduction in overall build or decommissioning process time
- >40% reduction in maintenance time
- >30% reduction in person hours on builds
Apply for this project
Contact the lead supervisor or programme team to discuss your interest. Full application instructions are on the How to apply page.
Funding Notes
£26,000 per year, plus a significant project, travel and training budget. Open to UK home students.