Rapid calculation method for stress distribution in the composite winding layer of hydrogen storage tanks based on generative networks

As global focus on sustainable energy solutions intensifies, hydrogen energy has emerged as a prominently discussed alternative fuel, celebrated for its clean energy properties. Hydrogen storage, a critical element of the hydrogen energy supply chain, has spotlighted composite material hydrogen storage cylinders due to their superior hydrogen storage mass density. However, operational hydrogen storage cylinders continue to encounter significant challenges, such as slow computational speeds and high resource consumption. This work introduces a generative network capable of rapidly predicting the stress distribution within the winding layer of composite material cylinders. Initially, a dataset of composite parameters and stress distribution map pair in the winding layer is constructed. Subsequently, a generative network is established. Through extensive training, a mapping relationship between the feature maps and interlayer stresses is developed, enabling the swift calculation of stress distribution in the winding layer. This advancement paves the way for the rapid prediction of material performance under service conditions, exerting a profound influence on the design of composite material cylinders and real-time applications.