2016
DOI: 10.1177/0731684416684211
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Effect of permeability characterization at different boundary and flow conditions on vacuum infusion process modeling

Abstract: Permeability characterization of a fabric preform is a key factor that affects the accuracy of process modeling of vacuum infusion. There are various flow types and boundary conditions (such as one-dimensional or radial flow under constant injection pressure or constant injection flow rate during unsaturated or saturated flow regimes) used in permeability measurement experiments in the literature. This study investigates the effect of using different flow and injection boundary conditions in permeability chara… Show more

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Cited by 19 publications
(16 citation statements)
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“…The literature on flow fronts provides the basis to evaluate the flow front behavior in hybrid laminates . The evaluation requires an analysis of the individual permeability of each fabric type .…”
Section: Methodsmentioning
confidence: 99%
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“…The literature on flow fronts provides the basis to evaluate the flow front behavior in hybrid laminates . The evaluation requires an analysis of the individual permeability of each fabric type .…”
Section: Methodsmentioning
confidence: 99%
“…The flow velocity u in Eq. 1 was used to describe the analytical model. The cross‐sectional area is determined by the thickness ( t ) and width of each fabric ( w ).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Fabrication of laminates with uniform thickness is another challenging aspect of conventional VARTM due to nonuniform evolution of compaction pressure on the preform during filling . Although compaction pressure can be equalized after filling by bleeding the excess resin from the exit , bleeding of sufficient resin is usually time consuming and may not always be completed before gelation.…”
Section: Introductionmentioning
confidence: 99%
“…Researchers used various methods to characterize the preform deformation during VARTM processes, e.g., the linear variable differential transducer [35,36], structured light scanner [37], and digital image correlation (DIC) [38]. To model the dynamically coupled flow moving and preform deforming, Acheson et al [39] modeled the preform as a nonlinear spring bed.…”
Section: Introductionmentioning
confidence: 99%