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Reference Number EP/Y024567/1
Title Multiscale data-driven failure prediction of hydrogen composite vessels under static and dynamic impact loading
Status Started
Energy Categories Hydrogen and Fuel Cells (Hydrogen, Hydrogen end uses (incl. combustion; excl. fuel cells)) 50%;
Hydrogen and Fuel Cells (Fuel Cells, Mobile applications) 50%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 50%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr D Yang

Sch of Engineering and Electronics
University of Edinburgh
Award Type Standard
Funding Source EPSRC
Start Date 18 September 2023
End Date 17 September 2025
Duration 24 months
Total Grant Value £200,512
Industrial Sectors
Region Scotland
Programme UKRI MSCA
 
Investigators Principal Investigator Dr D Yang , Sch of Engineering and Electronics, University of Edinburgh (100.000%)
Web Site
Objectives
Abstract As a key part of the fuel cell vehicles (FCVs), the development of safe, lightweight hydrogen storage vessels is critical for the range of the vehicles. Composite storage vessels has the potential to replace traditional metal vessels, enabling high hydrogen storage density per unit mass. However, due to the poor impact resistance and complex damage mechanisms of carbon fibre reinforced polymer (CFRP) composites, safety is still concerned once it is subjected to transverse impact. Therefore, it is imperative to design and optimise the hydrogen storage vessels based on safety requirements. How to replace the expensive and harsh dynamic test method, accurately predict the complex thermomechanical response of the composite storage vessel under the dynamic impact loading is critical for the development and deployment of new hydrogen composite vessels. Therefore, this EU Marie Curie fellowship aims to:(1) For the first time, create systematic and fundamental understanding of the failure strength and mechanisms of composites under much broader multiaxial loading conditions using a patented test rig;(2) Establish high fidelity RVE-based FE modelling of composite with ML identified uncertain material parameters to generate a full spectrum of failure predictions under multiaxial loading conditions;(3) Synergise the experimental data and numerical predictions to train data-driven ML tools for predicting the material failure envelope and informing the development of a modified failure criterion;(4) Implement the modified failure criterion and develop a multiscale virtual design and test tool for hydrogen composite vessels under impact loading as well as evaluate their CAI or TAI performance against experiments; (5) Develop and maintain a dedicated platform for dissemination of the developed virtual tool and promote virtual design and test of hydrogen composite vessels backed up by physical test
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