Manufacturing-informed, artificial-neural-network-assisted optimisation of Type IV composite pressure vessels through progressive damage modelling

Optimising the stacking sequence of Type IV composite overwrapped pressure vessels (COPVs) is challenging due to dome geometric complexity, progressive damage behaviour, and high computational cost. This study proposes an efficient optimisation framework for compressed natural gas (CNG) COPVs by coupling experimentally validated finite element modelling with machine learning. A high-fidelity finite element model incorporating dome-specific variations in fibre angle and layer thickness and progressive damage evolution via a VUMAT subroutine was used to generate a training database. Validation against four experimental prototypes yielded relative errors of 1.3–11.7% in burst pressure and 0.8–7.2% in composite…

• IH
  Inova Health System

  Award: 22/2551-0000839-9
• CD
  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
• CN
  Conselho Nacional de Desenvolvimento Científico e Tecnológico
• FD
  Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

  Award: 24/2551-0001477-2