Supervisors: Dr. Lukasz Figiel (WMG), Prof. James Sprittles (Maths), Dr. Gabriele Sosso (Chem.)

Summary:

Hydrogen is a zero-carbon emission fuel with the potential to decarbonise automotive and aerospace industries. Design of super-durable composite materials that can sustain harsh hydrogen environments is critical to achieving decarbonisation goals for the benefit of our planet. Multi-scale modelling methodologies that integrate modelling concepts from chemistry, physics, and engineering, and are accelerated with Machine Learning (ML), are crucial for accurate and efficient design of composites. This project will develop a radically-new predictive platform by combining mechanistic and data-driven approaches within the Bayesian framework. The platform will generate new knowledge and computer design tools, enabling wider exploitation of composite materials in hydrogen economy.

Project outcomes:

• Experimentally-validated multiscale modelling platform to predict reversible and irreversible physico-chemo-mechanical phenomena for composites under real-world conditions
• Surrogate models with uncertainty quantification of input parameters across the scales
• New understanding and design guidelines for the certification of composite materials for applications in hydrogen economy

Skills that the student will acquire:

• Linking continuum theories and data-driven approaches
• Using machine learning to accelerate multi-scale simulations.
• Bayesian framework for investigating the uncertainties of the model.
• Software development (e.g. Python).
• Soft skills (e.g. dissemination to external partners)

The successful candidate will be part of the Figiel Research Group, and will collaborate closely with co-supervisors at Mathematics and Chemistry Departments at the University of Warwick. The candidate will also have opportunity to interact closely with industry (e.g. Baker Hughes Ltd, National Composites Centre).

To discuss this project further, please contact: l.w.figiel@warwick.ac.uk