Advanced study into microdamage phenomena in composite materials for hydrogen economy

该项目旨在通过将先进的实验与理论相结合，更好地了解微观损伤背后的控制机制，并开发高压氢暴露下聚合物复合材料的预测模型。特别是，一个合适的机器学习方法将被用来分析从先进的显微镜，如X射线计算机断层扫描（CT）获得的实验数据，开发一个组合的机械和数据驱动的建模方法，描述材料的微损伤过程。这将形成在高压氢环境中的复合材料行为的改进的理论和预测模型的基础。本研究还旨在通过为我们的工业合作者提供有用的指导方针来了解高压气体环境下复合材料中微损伤的临界状态，从而提供非学术影响。因此，这将允许开发不那么保守、更可持续的设计协议，这将有助于开发未来使用聚合物复合材料的轻质氢基础设施。

This project aims to understand better the controlling mechanisms behind microscopic damage and develop a predictive model for polymer composites subject to high-pressure hydrogen exposure, by combining advanced experiments with theory. Particularly, a suitable Machine Learning approach will be utilized to analyse experimental data obtained from advanced microscopy such as X-ray computed tomography (CT), to develop a combined mechanistic and data-driven modelling methodology describing the material microdamage process. This will form the basis for an improved theory and predictive models of composite behaviour in high-pressure hydrogen environments. This study also aims to provide non-academic impact by proposing useful guidelines to our industrial collaborator about understanding critical states of microdamage in composite materials subject to high-pressure gaseous environments. Consequently, this will allow development of less conservative and more sustainable design protocols that will assist development of future light-weight hydrogen infrastructure with polymer composite materials.