{"title":"Deep learning-driven predictive tools for damage prediction and optimization in composite hydrogen storage tanks","authors":"","doi":"10.1016/j.coco.2024.102079","DOIUrl":null,"url":null,"abstract":"<div><p>This research presents a comprehensive framework for predicting the damage in lightweight composite high-pressure hydrogen storage tanks and optimizes their design to prevent failure. By integrating advanced analytical methods, numerical analysis, and deep learning techniques, we introduced a novel approach to enhance design optimization and damage prediction. The framework employs the 3D elasticity anisotropy theory to predict mechanical performance, incorporating failure criteria for accurate damage analysis. A parametric study was conducted to examine the effects of thickness, pressure, and diameter on the behavior of the tank. The analytical results were compared against finite element analysis using the WoundSim software, underscoring the significance of the modeling assumptions. Furthermore, we developed a new framework that combines deep neural networks with differential evolution optimization (DNN-DEO) to predict stress and damage in composite pressure vessels while identifying the optimal design parameters (pressure, radius, and thickness) to minimize failure risks and maintain high performance. A graphical user interface (GUI) was also designed to automate calculations and predictions, providing an intuitive tool for users. This integrated approach offers a powerful solution for optimizing the design and operation of lightweight composite hydrogen storage tanks, ensuring the reliability and efficiency of hydrogen storage systems.</p></div>","PeriodicalId":10533,"journal":{"name":"Composites Communications","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composites Communications","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452213924002705","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
引用次数: 0
Abstract
This research presents a comprehensive framework for predicting the damage in lightweight composite high-pressure hydrogen storage tanks and optimizes their design to prevent failure. By integrating advanced analytical methods, numerical analysis, and deep learning techniques, we introduced a novel approach to enhance design optimization and damage prediction. The framework employs the 3D elasticity anisotropy theory to predict mechanical performance, incorporating failure criteria for accurate damage analysis. A parametric study was conducted to examine the effects of thickness, pressure, and diameter on the behavior of the tank. The analytical results were compared against finite element analysis using the WoundSim software, underscoring the significance of the modeling assumptions. Furthermore, we developed a new framework that combines deep neural networks with differential evolution optimization (DNN-DEO) to predict stress and damage in composite pressure vessels while identifying the optimal design parameters (pressure, radius, and thickness) to minimize failure risks and maintain high performance. A graphical user interface (GUI) was also designed to automate calculations and predictions, providing an intuitive tool for users. This integrated approach offers a powerful solution for optimizing the design and operation of lightweight composite hydrogen storage tanks, ensuring the reliability and efficiency of hydrogen storage systems.
期刊介绍:
Composites Communications (Compos. Commun.) is a peer-reviewed journal publishing short communications and letters on the latest advances in composites science and technology. With a rapid review and publication process, its goal is to disseminate new knowledge promptly within the composites community. The journal welcomes manuscripts presenting creative concepts and new findings in design, state-of-the-art approaches in processing, synthesis, characterization, and mechanics modeling. In addition to traditional fiber-/particulate-reinforced engineering composites, it encourages submissions on composites with exceptional physical, mechanical, and fracture properties, as well as those with unique functions and significant application potential. This includes biomimetic and bio-inspired composites for biomedical applications, functional nano-composites for thermal management and energy applications, and composites designed for extreme service environments.