Statistical modeling of in-plane permeability of non-woven random fibrous reinforcement

Abstract

International audienceA methodology has been proposed for statistical characterization of transport behavior of a typical random fibrous medium, i.e. the Chopped Strand Mat (CSM). For any given digital images of fabric sample, statistical description of the random microstructure is employed to evaluate the permeability field, in the framework of the statistical continuum approach. By choosing suitable sampling strategy, the evaluated permeability field can be used to predict the realistic fluctuation in the flow pattern in the RTM mold-filling process with a high accuracy, as validated by the radial injection experiments. The method can be generalized to other random fibrous media. Using a database of CSM samples, statistical characterization of fiber volume fraction and permeability fields is performed respectively. Important statistical properties, e.g. the RVE size, marginal PDF and correlation length have been provided for the transport properties of the CSM, as the basis for the input data for stochastic simulation of composite processing

On the statistics of transverse permeability of randomly distributed fibers

“…These families of distributions (GEV, power law, pareto, lognormal) have been found to be useful for stochastic modelling [32,33,34,35,36] when inputs such as permeability follow heavy-tailed distributions. However, the use of 210 mixture model is more complex than GEV.…”

“…where D h = 4/d [11] is the hydraulic diameter and ψ CK is the Kozeny factor. Substituting the expression of the hydraulic diameter in equation (15) and…”

“…In those studies the fibers are considered regularly arranged and the effect of random distribution of the fiber is not taken into account.The uncertainties associated with permeability originated from fiber size distribution, porosity and compaction, will cause significant errors [14] when simulating the transport properties of fluid in porous media. Although previous studies have assumed permeability as a random variable in porous media modeling, the complexity involved in establishing a mutual relationship between micro-and macro-scale uncertainties is not common, with only one paper so far [15] proposing a probability density function for permeability.…”

Three-dimensional reconstruction of resin flow using capacitance sensor data assimilation during a liquid composite molding process: A numerical study

“…Statistical modeling of permeability variations [5] or stochastic simulation of resin flow [6] has also been studied to further improve optimization of the processing conditions. However, it is almost impossible to control and optimize many factors involved in an LCM process prior to manufacturing.…”

Uncertainty in geometry of fibre preforms manufactured with Automated Dry Fibre Placement and its effects on permeability

“…Experimental data from those studies were used in the models of textile reinforcements with variability in the geometry and used for prediction of probability of draping outcomes, 8 distribution of mechanical properties 12 and permeability. 7,[16][17][18][19][20] In particular, it was demonstrated that the mould fill time can be several times longer than the fill time in the deterministic case. 18 The geometry of a dry reinforcement can be described by a model where tow position, tow width and other details are described by a set of random variables, which may be mutually independent or can be correlated.…”