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

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...

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Veröffentlicht in:Composites. Part A, Applied science and manufacturing Jg. 167; S. 107397
Hauptverfasser: 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.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2023
Elsevier
Schlagworte:
A fabric/textile
A tow
A. Fabrics/textiles
B permeability
Besin flow
Boundary conditions
C computational modeling
C. Computational modelling
Computational modelling
E resin flow
Engineering Sciences
exercise
Fabric/textiles
Fabrics/textiles
Forecasting
Materials
Mechanical permeability
Numerical methods
Permeability
Permeability identification
Permeability prediction
prediction
Resin flows
Textiles
Tow
A. Tow
C. Computational modelling
B. Permeability
A. Fabrics/textiles
E. Resin flow
Resin flow
Permeability
Computational modeling
Tow
Fabrics/textiles
ISSN:1359-835X, 1878-5840, 1878-5840
Online-Zugang:Volltext
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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.
AbstractList 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. 
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.
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.
ArticleNumber 107397
Author Drapier, S.
Akhatov, I.
Silva, L.
Abaimov, S.
Shishkina, O.
Asiaban, N.
Vorobyev, R.
Umer, R.
Swolfs, Y.
Moulin, N.
Leygue, A.
Mahato, B.
Sergeichev, I.
Syerko, E.
Orgéas, L.
Aissa, N.
May, D.
Digonnet, H.
Endruweit, A.
Martínez-Lera, P.
Binetruy, C.
Middendorf, P.
Kandinskii, R.
Michaud, V.
Shakoor, M.
Broggi, G.
Guilloux, A.
Dittmann, J.
Ali, M.
Schmidt, T.
Caglar, B.
Rouhi, M.
Park, C.H.
Lomov, S.
Advani, S.G.
Matveev, M.
Tahani, M.
Bruchon, J.
Rief, S.
Vanclooster, K.
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Keywords A. Tow
C. Computational modelling
B. Permeability
A. Fabrics/textiles
E. Resin flow
Resin flow
Permeability
Computational modeling
Tow
Fabrics/textiles
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Publisher Elsevier Ltd
Elsevier
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Snippet Permeability measurements of engineering textiles exhibit large variability as no standardization method currently exists; numerical permeability prediction is...
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SubjectTerms A fabric/textile
A tow
A. Fabrics/textiles
B permeability
Besin flow
Boundary conditions
C computational modeling
C. Computational modelling
Computational modelling
E resin flow
Engineering Sciences
exercise
Fabric/textiles
Fabrics/textiles
Forecasting
Materials
Mechanical permeability
Numerical methods
Permeability
Permeability identification
Permeability prediction
prediction
Resin flows
Textiles
Tow
Title Benchmark exercise on image-based permeability determination of engineering textiles: Microscale predictions
URI https://dx.doi.org/10.1016/j.compositesa.2022.107397
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