Benchmark exercise on image-based permeability determination of engineering textiles: Microscale predictions

Syerko, E.; Schmidt, T.; May, D.; Binetruy, C.; Advani, S.G.; Lomov, S.; Silva, L.; Abaimov, S.; Aissa, N.; Akhatov, I.; Ali, M.; Asiaban, N.; Broggi, G.; Bruchon, J.; Caglar, B.; Digonnet, H.; Dittmann, J.; Drapier, S.; Endruweit, A.; Guilloux, A.; Kandinskii, R.; Leygue, A.; Mahato, B.; Martínez-Lera, P.; Matveev, M.; Michaud, V.; Middendorf, P.; Moulin, N.; Orgéas, L.; Park, C.H.; Rief, S.; Rouhi, M.; Sergeichev, I.; Shakoor, M.; Shishkina, O.; Swolfs, Y.; Tahani, M.; Umer, R.; Vanclooster, K.; Vorobyev, R.

Abstract

Permeability measurements of engineering textiles exhibit large variability as no standardization method currently exists; numerical permeability prediction is thus an attractive alternative. It has all advantages of virtual material characterization, including the possibility to study the impact of material variability and small-scale parameters. This paper presents the results of an international virtual permeability benchmark, which is a first contribution to permeability predictions for fibrous reinforcements based on real images. In this first stage, the focus was on the microscale computation of fiber bundle permeability. In total 16 participants provided 50 results using different numerical methods, boundary conditions, permeability identification techniques. The scatter of the predicted axial permeability after the elimination of inconsistent results was found to be smaller (14%) than that of the transverse permeability (∼24%). Dominant effects on the permeability were found to be the boundary conditions in tangential direction, number of sub-domains used in the renormalization approach, and the permeability identification technique.

Citation

Syerko, E., Schmidt, T., May, D., Binetruy, C., Advani, S., Lomov, S., …Vorobyev, R. (2023). Benchmark exercise on image-based permeability determination of engineering textiles: Microscale predictions. Composites Part A: Applied Science and Manufacturing, 167, Article 107397. https://doi.org/10.1016/j.compositesa.2022.107397

• Journal Article Type: Article
• Acceptance Date: Dec 21, 2022
• Online Publication Date: Jan 6, 2023
• Publication Date: 2023-04
• Deposit Date: Jan 11, 2023
• Publicly Available Date: Jan 7, 2024
• Journal: Composites Part A: Applied Science and Manufacturing
• Print ISSN: 1359-835X
• Electronic ISSN: 1878-5840
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 167
• Article Number: 107397
• DOI: https://doi.org/10.1016/j.compositesa.2022.107397
• Public URL: https://nottingham-repository.worktribe.com/output/15436382
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1359835X22005784