AI-driven advances in composite materials for hydrogen storage vessels: A review

Résumé fourni par la source

This review provides a comprehensive examination of artificial intelligence methods applied to the design, optimization, and performance prediction of composite-based hydrogen storage vessels, with a focus on composite overwrapped pressure vessels. Targeted at researchers, engineers, and industrial stakeholders in materials science, mechanical engineering, and renewable energy sectors, the paper aims to bridge traditional mechanical modeling with evolving AI tools, while emphasizing alignment with standardization and certification requirements to enhance safety, efficiency, and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types, their material compositions, and key design challenges, including high-pressure durability, weight reduction, hydrogen embrittlement, leakage prevention, and environmental sustainability. It then analyzes conventional approaches, such as finite element analysis, multiscale modeling, and experimental testing, which effectively address aspects like failure modes, fracture strength, liner damage, dome thickness, winding angle effects, crash behavior, crack propagation, charging/discharging dynamics, burst pressure, durability, reliability, and fatigue life. On the other hand, it has been shown that to optimize and predict the characteristics of hydrogen storage vessels, it is necessary to combine the conventional methods with artificial intelligence methods, as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps, the paper evaluates hybrid frameworks that integrate traditional techniques with AI, including machine learning, deep learning, artificial neural networks, evolutionary algorithms, and fuzzy logic. Recent studies demonstrate AI's efficacy in failure prediction, design optimization to mitigate structural risks, structural health monitoring, material property evaluation, burst pressure forecasting, crack detection, composite lay-up arrangement, weight minimization, material distribution enhancement, metal foam ratio optimization, and optimal material selection. By synthesizing these advancements, this work underscores AI's potential to accelerate development, reduce costs, and improve HSV performance, while advocating for physics-informed models, robust datasets, and regulatory alignment to facilitate industrial adoption.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.