Hydrogen fuel cell technology is securing a place in the future of advanced mobility and the energy revolution, as engineers explore multiple paths in the quest for decarbonization. The feasibility of hydrogen-based fuel cell vehicles particularly relies on the development of safe, lightweight and cost-competitive solutions for hydrogen storage. After the demonstration of hundreds of prototype vehicles, today, commercial hydrogen tanks are in the first stages of market introduction, adopting configurations that use composite materials. However, production rates remain low and costs high. This paper intends to provide an insight into the evolving scenario of solutions for hydrogen storage in the transportation sector. Current applications in different sectors of transport are covered, focusing on their individual requirements. Furthermore, this work addresses the efforts to produce economically attractive composite tanks, discussing the challenges surrounding material choices and manufacturing practices, as well as cutting-edge trends pursued by research and development teams. Key issues in the design and analysis of hydrogen tanks are also discussed. Finally, testing and certification requirements are debated once they play a vital role in industry acceptance.

Buckling is a critical failure mode for aircraft composite panels. Therefore, determining the critical buckling load is essential for properly modeling. To optimize composite laminate structures, it is important to know the strength of the laminate, which can be obtained through statistical analyses based on test data. The resultant strength value is known as a design allowable. This paper aims to establish an approach to evaluate two types of statistically determined buckling allowables, A-Basis and B-Basis, for an aircraft composite wing. In this study, a finite element model of a composite semi-wing was used to perform linear buckling simulations. Six input parameters were initially selected as relevant to affect the buckling strength of the semi-wing: four material properties and two geometric parameters. A set of Monte Carlo simulations was conducted varying these parameters of interest, followed by a global sensitivity analysis using Sobol indices to identify the influence of each one, individually (first-order) and in pairs (second-order). A surrogate model based on artificial neural networks was then trained using data from the Monte Carlo simulations. Finally, this surrogate model was used to define the A-Basis and B-Basis allowables for a critical loading case.