1.Structural Analysis of Pore Size Distribution of Nonwovens

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Structural analysis of pore size distribution of nonwovensAmit Rawala

a Department of Textile Technology, Indian Institute of Technology, New Delhi, India

First published on: 11 February 2010

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The Journal of The Textile InstituteVol. 101, No. 4, April 2010, 350–359

Structural analysis of pore size distribution of nonwovens†

Amit Rawal∗

Department of Textile Technology, Indian Institute of Technology, New Delhi 110016, India

(Received 10 January 2008; final version received 20 March 2008)

Pore size distribution is a prerequisite to investigate any transport phenomena, especially in a porous structure such asnonwovens. The pores inside the nonwovens are highly complex in terms of the sizes, shapes and the capillary geometries.The majority of existing theories/models of pore size distribution of nonwovens do not account for the fibre orientationdistribution characteristics. In this research work, the model for predicting the pore size distribution of nonwoven structureshas been developed by combining the stochastic and stereological or geometrical probability approaches. These techniqueshave incorporated the effects of fibre orientation characteristics in nonwoven structures. The analytical model formulatedis compared with the existing theories to predict the pore size distribution of nonwoven structures. A comparison is alsomade between the experimental and theoretical pore size distributions of spun-bonded and needle-punched nonwovens. Theeffect of various fibre and fabric parameters including fibre volume fraction, fibre orientation distribution characteristics andnumber of layers on pore size distribution of nonwoven structures has been investigated.

Keywords: nonwovens; pore size distribution; fibre orientation; transport phenomenon; capillary; geometrical probability

IntroductionPore size distribution is a prerequisite to investigate anytransport phenomena, especially in a porous structure suchas nonwovens (Pan & Zhong, 2006). The list of transportprocesses includes filtration, separation, resin impregna-tion, wetting and wicking. The pores inside the nonwovensare highly complex in terms of the sizes, shapes and the cap-illary geometries. In the past, the concept of hydraulic radiushas been applied to reduce the irregular shapes of the poresto circular shapes by matching the same volume flow as anarray of irregular-shaped pores (Lawrence & Shen, 2000).In general, the pore geometry depends on numerous param-eters, namely, fibre properties, processing conditions andfabric characteristics (Lifshutz, 2005; Savel’eva, Dedov,Bokova, & Andrianova, 2005; Simmonds, Bomberger, &Bryner, 2007). It is well known that the flow in the mediumoccurs if and only if the pores are interconnected (Fatt,1956). Highly porous media such as nonwovens tend to bemore interconnected and should have a lower tortuosity,i.e., the ratio of path length of the flow to the thickness ofmedium (Vereoot & Cattle, 2003).

Pore geometry in nonwovens can be represented as athree-dimensional (3D) network of pore bodies connectedby fibres. A simple analogy is to represent string and beadas fibre and pore, respectively, and the problem is how toarrange these strings of beads in a box (Dullien, 1975;Yanuka, Dullien, & Elrick, 1986). The problem becomes

∗Email: [email protected]; [email protected]†This paper, in a slightly different version, was first published in the proceedings of EDANA’s 2008 Nonwovens Research Academy (NRA)and publicly presented on the occasion of this event in Chemnitz, Germany. It is reproduced with permission

further complex if the material has a hierarchical structureconsisting of pores of different length scales, i.e., macro-and micro-porosity (Vocka & Dubois, 2000). Numerousmodels have been employed to predict the pore size of vari-ous kinds of porous structures including capillary pressuremethod, stochastic model, deterministic approach, spherepack models and stereological or geometrical probabilitymodel. Capillary pressure and stochastic are the most pop-ular models for determining the pore size distribution ofnonwoven fabrics.

Fatt (1956) simplified the 3D network of cylindricaltubes by two-dimensional polygonal networks consistingof square elemental units in addition to the overlappinghexagonal units. The capillary pressure curves were deter-mined by computing the pressure exceeding the capillarypressure of the largest pore of the connecting smaller pores.The theory was valid mainly for granular structures. Sub-sequently, the theory of capillary pressure was employed tothe fabrics for predicting the pore size distribution (Steele,1958). Using the principle of capillary pressure, the bubblepoint method was formulated, and it has been successfullyapplied to determine the pore size distribution of varioustypes of nonwoven geotextiles (Bhatia & Smith, 1995).On the other hand, the fibrous filters were simulated usingrandom line network, and the polygon area distributionwas approximated by log-normal distribution (Piekaar& Clarenburg, 1967). Similar expressions were obtained

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The Journal of The Textile Institute 351

for random network lines by Corte and Lloyd (1966),and they established that the mean number of sides perpolygon is four and the free fibre length between thecrossover points are distributed according to negativeexponential distribution (Miles, 1964). More recently,Dodson and his colleagues established that the pore sizedistribution exhibited gamma distribution in a randomfibre network (Dodson, Handley, Oba, & Sampson, 2003;Dodson & Sampson, 1996, 1997; Sampson, 2001, 2003).Similarly, the gamma distribution of pore radii has alsobeen extended in anisotropic assembly of fibres (Castro& Ostoja-Starzewski, 2000). Furthermore, probabilisticmodels of inscribed circles in the polygons have been wellrepresented as a Poisson’s distribution, and hence, the poresize distribution of nonwoven structures has been predictedand a relationship between the fibre and fabric propertieswas formulated (Faure, Gourc, & Gendrin, 1990; Lombard,Rollin, & Wolff, 1989; Matheron, 1972; Rollin, Denis,Estaque, & Masounave, 1982). The Monte-Carlo techniquehas also been applied to simulate the fibre network byassuming the nonwoven structure in the form of layers andobtained pore size distribution numerically (Abdel-Ghani& Davies, 1985). In addition, semi-empirical models ofpore size distribution have been developed for nonwovenstructures (Giroud, 1996; Jaksic & Jaksic, 2007). The maindrawback of the above theories/models is that the effectof fibre orientation has not been accounted to compute thepore size distribution of nonwoven structures. Nevertheless,Castro and Ostoja-Starzewski (2000) have proposed thatthe pore size distribution of anisotropic fibrous assembliesis independent of effect of fibre orientation, but the theorieswere not experimentally validated. However, it has beendemonstrated that fibre orientation is the key parameterinfluencing the pore size distribution of nonwoven andrelated structures (Kim & Pourdeyhimi, 2000; Komori &Makashima, 1979; Ollson & Pihl, 1952; Velu, Ghosh, &Abdelfattah, 2004). Therefore, the objective of the researchwork is to develop a simple model for predicting the poresize distribution of nonwoven structures that includesthe effect of fibre orientation. The analytical model iscompared with the existing theories to determine the poresize distribution of nonwoven structures. Subsequently, themodel is validated with the experimental results.

Theoretical approaches

A nonwoven is a 3D structure consisting of layers of fibresoriented in certain directions. This 3D model of nonwovenstructure can be easily developed by assuming the fibres tobe stacked in elementary longitudinal planes (i.e. parallel tothe plane of sheet), also known as longitudinal porometrymodel (Faure et al., 1990). It will be shown later that thistype of model is highly useful in predicting the pore sizedistribution as it can easily capture the information aboutfibre orientation in a nonwoven structure. The longitudinalporometry models that have been used in the past for pre-

dicting the pore size distributions of nonwoven structuresare briefly described below.

Faure model of pore size distribution of nonwovenstructures

Faure and colleagues (Faure et al., 1990; Faure, Gourc,Millot, & Sunjoto, 1986) proposed a model for predictingthe pore size distribution of nonwoven geotextiles based onPoissonian polyhedra theory (Matheron, 1972). The modelis based upon Poisson line networks where the lines sim-ulating the network of fibres are randomly oriented in theplane. The inter-fibre spaces or pores between the straightlines consist of convex polygons. The cumulative probabil-ity of obtaining an inscribed circle between the polygons ofdiameter equal to or less than d are shown below.

G(d) = 1 −(

2 + χ (d + Df )

2 + χDf

)2

e−χd (1)

and

χ = 4µ

πTgDfρf, (2)

where χ is the specific length representing the total lengthof lines per unit area; d is the inscribed circle diameter; µ isthe mass per unit area of fabric; Tg is the fabric thickness;Df is the fibre diameter; and ρf is the density of the fibre.

The pores were assumed as a disc or an elementaryconduit in each layer with axis perpendicular to the planeof a sheet such that the length of each disc is defined as thefibre diameter. Each elementary conduit connects to oneand only one elementary conduit in the successive layer ofa nonwoven structure. Therefore, the gradation of conduitsprovides the cumulative probability of passage of particlesthrough the layers of nonwovens.

Q(d) = 1 − [1 − G(d)]Tg/Df (3)

where Q(d) is the probability of a particle with diameter d,passing through a pore channel in the nonwoven.

Lombard model of pore size distribution ofnonwoven structures

Lombard et al. (1989) used the same analogy of Poisson’spolyhedra theory for obtaining the expressions for prob-ability of diameter distribution of a circle inscribed in apolygon, i.e. K(d).

K(d) =(

σ 2d2

4+ σd + 1

)exp (−σd) (4)

and

σ = 8µ

πTgDfρf, (5)


352 A. Rawal

where σ is the specific length, representing the total lengthof lines per unit area.

Equation (4) shows that the particle of a given diametercan be retained; however, the particle should travel throughthe layers of the nonwoven. Therefore,

Ff (d) = 1 − [K(d)]Tg/2Df , (6)

where Ff (d) is the cumulative probability of passage ofparticles of diameter d through the layers of the nonwoven.

One of the fundamental differences between the expres-sions for obtaining the cumulative probability of passage ofparticles through the layers is that the effective layer thick-ness or thickness of each layer is considered to be fibrediameter and twice the fibre diameter in Faure et al. (1986,1990) and Lombard et al. (1989) analysis, respectively.

Theoretical model for pore size distribution ofnonwoven structures

In the above models, the specific length of fibre (total lengthof fibre per unit area) is distributed stochastically, and themethodology has not captured about the information oforientation of fibres in the structure. However, the stereo-logical or geometrical probability approach can include theeffect of fibre orientation on pore size distributions. Thereare two ways of dealing the problem through geometricalprobability approach. The first method is based on calculat-ing the total length of fibres in a given volume of the planeby considering the intersection between the orientation offibre and sectioning plane (Komori & Makashima, 1978).A fibre of the orientation (θ ,ϕ) is intersected by the planewith an orientation (�,�); therefore, the total number of in-tersections, υ(�,�), of fibres having all possible directionson a unit area of the secant plane is shown below:

υ(�,�) = L · B(�,�) (7)

and

B(�,�) =∫ π

0dθ

∫ π

0dϕA(θ, ϕ,�,�)(θ, ϕ) sin θ

A(θ, ϕ,�,�) = |sin θ sin � cos(ϕ − �) + cos θ cos �| ,

where L is the total length of a fibre in a given volume, and(θ ,ϕ) is the orientation distribution function of fibres.

The total length of the fibre in each layer can be calcu-lated by determining the number of intersections, υ(�,�),experimentally. Here, each layer has a thickness of twicethe fibre diameter. Subsequently, the total length of fibresin a given volume of the plane can be incorporated intoany stochastic model for the passage of particle throughthe polygons. However, it is a cumbersome process to de-termine the number of intersections experimentally. The

second method is much simpler and based upon the com-bination of gamma distribution of polygon-inscribed cir-cles relating the fibre orientation distribution characteris-tics. The following assumptions have been made to simplifythe model for pore size distribution of nonwoven structures.

� The constituent fibres used in the nonwoven structurehave identical geometrical properties.

� The decrease in the length of the fibres due to theintersection or superposition has been neglected.

� The nonwoven structure has been divided into ele-mentary planes of equal thickness and has similarfibre orientation distribution characteristics.

� The effect of fibre crimp has not been taken intoaccount because Kim and Pourdeyhimi (2000) haveclearly shown that the effect of fibre crimp has neg-ligible influence on the pore size distribution of non-woven structures.

In general, the gamma distribution has the followingprobability density function (Dodson & Sampson, 1996,1997):

f (x) = ωk

�(k)xk−1e−ωx (8)

and

�(k) = (k − 1)!,

where x is any variable; k is the shape parameter; and ω isthe scale or coverage parameter, which will be defined later.

Based on Castro and Ostoja-Starzewski (2000) analysis,it has been shown that the value of shape parameter (k) is3,

f (x) = ω3

2x2e−ωx. (9)

Therefore, the cumulative probability, F (d), of obtainingan inscribed circle between the polygons of diameter equalto or less than d is given below:

F (d) =∫ d

0

ω3

2x2e−ωxdx. (10)

Integrating by parts has yielded the following expression:

F (d) = 1 −(

1 + ωd + ω2d2

2

)e−ωd . (11)

Equation (11) shows that in each layer, the particle of diam-eter, dp, will not be retained on convex polygons if dp ≤ d.However, the cumulative probability for the particle of di-ameter greater than the pore diameter, i.e. dp > d, in each



layer is complement to 1 of F (d). In other words, the parti-cle of diameter (dp) will be retained on the inscribed circleof diameter d. Hence,

H (d) = [1 − F (d)] , (12)

where H (d) is the probability of a particle with diameter d

retaining on each layer of nonwoven.The cumulative probability for the particle to be retained

on a given number of layers (N ) is shown below.

Hf (d) = [1 − F (d)]N , (13)

where Hf (d) is the cumulative probability of a particle withdiameter d retaining on the layers of nonwoven.

Hence, the cumulative probability of a particle withdiameter d not being retained on the layers of nonwoven,i.e., Ff (d) or the particle with diameter d passing throughthe layers of a nonwoven is shown below.

Ff (d) = 1 − Hf (d). (14)

Combining the Equations (11)–(14) has yielded the follow-ing expression:

Ff (d) = 1 −[(

1 + ωd + ω2d2

2

)e−ωd

]N

. (15)

To calculate the number of layers, it is assumed that eachlayer is stacked in an elementary plane and the numberof layers is defined as the ratio of total thickness of thenonwoven to the effective thickness of elementary plane(Faure et al., 1986, 1990; Lombard et al., 1989). Accordingto Faure et al. (1986), the effective thickness of elementaryplane has a significant influence on their model, and in ourmodel, each layer is assumed to have a thickness of twicethe fibre diameter, as shown in Figure 1. For a particle totravel through the layers of nonwoven, it is assumed thateach elementary plane consists of two fibres; therefore,the structure must consist of Tg/2Df planes where Tg isthe thickness of the fabric and Df is the fibre diameter.Hence, the following relation to determine the cumulativeprobability of passage of particles through the layers ofnonwoven is obtained:

Ff (d) = 1 −[(

1 + ωd + ω2d2

2

)e−ωd

]Tg/2Df

. (16)

It is important to quantify the coverage parameter (ω) as itreveals the shape and dimension of the pore in the struc-ture. The area and shape of irregular polygon, specifically inanisotropic fibrous assemblies, are susceptible to the num-ber of the intersections and the angle formed between thetwo fibres. In other words, the number of intersections be-tween the fibres and the orientation of the fibre in a structure

Figure 1. Pores modelled in a layer of fibres.

control the geometry of an irregular polygon, and hence,the largest diameter of the particle passing through a pore.Strictly speaking, the number of fibres forming a polygonwill have the same number of fibre intersections.

According to Pan, Chen, Seo, and Backer (1997) andRawal, Lomov, Ngo, and Vankerrebrouck (2007), the num-ber of intersections formed in a volume, dVj , having a unitcross-sectional area (Aj = 1), is limited by two planes nor-mal to the test direction. The volume, dVj , has the thicknesscorresponding to the average distance between the intersec-tions projected on the planar direction, j . The number ofintersections, ndVj

, is calculated inside the volume, dVj , as-suming that the fibres are distributed uniformly in the space(Rawal et al., 2007), and hence,

ndVj= 2Vf

πD2f

Kj (17)

and

Kj =∫ π/2

0sin2 θdθ

∫ π/2

−π/2(θ, ϕ) |cos ϕ| dϕ, (18)

where Df is the fibre diameter; Vf is the fibre volume frac-tion; (θ ,ϕ) is the orientation distribution function of fibres;and Kj is the directional parameter or geometric coefficientwhen the average distance between the intersections is pro-jected on the planar direction j . For an in-plane distribution(θ, ϕ) = ϕ(ϕ) · δ (θ − π/2), Equation (18) changes to


354 A. Rawal

Table 1. Fibre properties used in the production of nonwovenstructures.

Properties Spun-bonded Needle-punched

Type of fibre Polyethylene PolyesterFibre diameter (µm) 20 23.5Fibre density (g/cm3) 0.95 1.38

the following expression:

Kj =∫ π/2

−π/2|cos ϕ| ϕ(ϕ)dϕ, (19)

where ϕ(ϕ) is an in-plane distribution of fibres (ϕ).The coverage parameter (ω) can be defined as the prod-

uct of number of intersections formed in a volume (dVj )having a unit cross-sectional area (ndVj

) and thickness ofeach layer (twice the fibre diameter). Therefore,

ω = ndVj(2Df ) = 4VfKj

πDf. (20)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

–90 –80 –70 –60 –50–40 –30 –20 –10 0 10 20 30 40 50 60 70 80 90

Fibre orientation angle (o)

Rel

ativ

e fr

equ

ency

(a)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

–90 –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 70 80 90

Fibre orientation angle (o)

Rel

ativ

e fr

equ

ency

(b)

Figure 2. Fibre orientation distributions for (a) spun-bonded and (b) needle-punched nonwoven structures.

Table 2. Properties of nonwoven structures.

Properties Spun-bonded Needle-punched

Mass per unit area (g/m2) 75 296Thickness (mm) 0.89 3.47

Also,

Vf = µ

ρfTg, (21)

where µ is the mass per unit area of fabric; Tg is the fabricthickness; and ρf is the density of fibre.

From Equations (20) and (21),

ω = 4µKj

πTgDfρf. (22)

The expressions for Faure’s specific length as shown inEquation (2) and coverage parameter stated in Equation(22) are similar except that the directional parameter isincorporated in computing the coverage parameter. Nev-ertheless, Equations (16) and (22) involve basic fibre and



0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600

Pore diameter (µm)

Cu

mu

lati

ve f

req

uen

cies

Lombard's Model

Faure's Model

Experimental

Theoretical

Figure 3. Comparison between theoretical and experimental pore size distribution of a spun-bonded nonwoven structure.

fabric properties including the effect of fibre orientationdistribution characteristics.

Experimental

The reported work is based upon two types of nonwovens,i.e., spun-bonded (supplied by the Technical University ofLiberec) and needle-punched structures. Polyethylene andpolyester fibres have been used in the production of spun-bonded and needle-punched nonwoven structures, respec-tively, and the properties of constituent fibres are shownin Table 1. A needle-punched structure was produced byopening the fibres by carding and, subsequently, prefer-entially orientated in the cross-machine direction using across-lapper. Figure 2 shows the histograms of relative fre-quency of fibres with respect to the machine direction (0◦)of spun-bonded and needle-punched nonwoven structures.The fabric properties are also given in Table 2. The poresizes of the nonwoven fabrics were experimentally deter-mined by capillary flow porometer based on the principleof liquid extrusion porosimetry technique. In this method,

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500

Pore diameter (µm)

Cu

mu

lati

ve f

req

uen

cies

Lombard's Model

Faure's Model

Experimental

Theoretical

Figure 4. Comparison between theoretical and experimental pore size distribution of a needle-punched nonwoven structure.

a specimen of 2.1 cm in diameter is saturated with wettingliquid (Galwick) of low surface tension (15.9 dynes/cm)and unidirectional air pressure is applied to the saturatedspecimen to force the liquid on the other side. An increasein air pressure causes the bubble to escape from the largestpore initially, and a further increase in air pressure resultsin removal of the bubbles from smaller pores. This impliesthat the liquid from the largest pores would be emptiedat the lowest pressure and the gas flow through the speci-men would be initiated. Further details of liquid extrusionporosimetry technique are given by Jena and Gupta (2003).

Results and discussion

A comparison between the theoretical and experimentalresults is given below. In addition, the effect of some fibreand fabric parameters has also been analysed.

Comparison of theoretical and experimental results

The pore size distributions of spun-bonded and needle-punched nonwoven structures are theoretically obtained


356 A. Rawal

Figure 5. Top view of a spun-bonded nonwoven structure.

using Equations (16) and (22). Subsequently, a comparisonis made between the theoretical and experimental results ofpore size distribution of spun-bonded and needle-punchednonwovens, as shown in Figures 3 and 4, respectively. Ingeneral, there is an excellent correlation (R = 0.99) be-tween theoretical and experimental results of pore size dis-tribution of spun-bonded and needle-punched nonwovens.The differences between the two sets of results are due to thefollowing reasons. Firstly, some of the pores are not convex,that leads to an unavoidable error. In case of needle-punchednonwovens, there are fibres orientated through the plane (zdirection); however, the ratio of fibres in the z direction is asmall fraction of the total number of fibres and the in-planeorientation of the fibre still dictates the structural charac-teristics of any nonwoven (Mao, Russell, & Pourdeyhimi,2007). Secondly, lighter fabrics, especially spun-bondednonwovens, are more sensitive to structural deformation asthe distance between the bond points or free fibre length is

Figure 6. Relationship between cumulative probability of passage of particles and directional parameter for a nonwoven of area density75 g/m2 with varying fibre orientation distributions.

quite high, and it is also evident from Figure 5. Therefore,the particles can move laterally forming a larger passage inthe structure (Lombard et al., 1989). Thirdly, there is a fibrediameter variation in the case of a spun-bonded nonwovenstructure that can become significant at the microscale andcan lead to inevitable errors as it has been assumed that thegeometrical properties of constituent fibres are the same.Similarly, there may be some variation in the physical char-acteristics of nonwovens including mass-per-unit area andthickness.

The pore size distribution of spun-bonded and needle-punched nonwoven structures is also predicted using themodels of Lombard et al. (1989) and Faure et al. (1990),and a comparison is made between the theoretical and ex-perimental results, as shown in Figures 3 and 4. It has beenfound that these models have largely underestimated thepore size distribution of nonwovens and, therefore, maynot be suitable for predicting the pore size distribution ofnonwoven structures.

Effect of fibre and fabric parameters on the poresize distribution of nonwoven structures

Equations (16) and (22) show that the probability of passageof particles through the layers of nonwoven is dependent onfibre volume fraction, directional parameter or fibre ori-entation distribution characteristics and number of layers.For a given fibre type, the above parameters can be easilyvaried. Theoretically, the value of directional parameter is0.6, assuming that the fibres are uniformly and randomlydistributed in the structure. Figure 6 shows the passageof particles through the layers of nonwoven as a functionof directional parameter for a fictive nonwoven keepingother parameters constant. It is clearly shown that thereis a shift in pore size distribution curves, i.e., pore sizedecreases as the anisotropy increases. It is contradictory to



Figure 7. Probability of particles passing through the fictive spun-bonded nonwovens having uniform randomly oriented fibres (diameter30 µm) as a function of pore diameter and fibre volume fraction.

the previous theories proposed by Castro and Ostoja-Starzewski (2000) in which the pore size distribution pre-dicted is not affected by fibre orientation distribution.However, the relationship obtained between pore size andanisotropic characteristics of nonwovens is found to be sim-

Figure 8. Probability of particles passing through the fictive spun-bonded nonwovens having uniform randomly oriented fibres (diameter30 µm) as a function of pore diameter and number of layers.

ilar to that simulated by Kim and Pourdeyhimi (2000) andVelu et al. (2004). The researchers have demonstrated thatthe pore radius decreases with an increase in the anisotropy.Furthermore, the combined effect of fibre volume frac-tion and particle diameter on the probability of passage


358 A. Rawal

of the particles through spun-bonded nonwoven has alsobeen analysed, as shown in Figure 7. An increase in fibrevolume fraction decreases the pore size as number of fibreincreases in each layer of a nonwoven structure. Similarbehaviour has been observed by increasing the number oflayers in a nonwoven structure, as shown in Figure 8.

Conclusions

In this research work, an analytical model of pore size dis-tribution of nonwovens has been developed based on thegamma distribution of circles inscribed in the convex poly-gons formed between the straight lines. Subsequently, themodel has been related to the fibre orientation character-istics of nonwovens by calculating the number of fibreintersections forming a polygon on the given volume ofthe plane. In general, there is a good agreement betweenthe theoretical and experimental results of pore size dis-tribution of nonwoven structures. A comparison betweenthe existing analytical models of pore size distribution ofnonwovens and experimental results has revealed that thesemodels have largely underestimated and, therefore, may notbe suitable for predicting the pore size distribution of thesestructures (Faure et al., 1990; Lombard et al., 1989). It isalso shown that pore size decreases as the anisotropy in-creases and it is contrary to the previous research work inwhich fibre orientation has no effect on pore size distri-bution of fibrous assemblies (Castro & Ostoja-Starzewski,2000; Komori & Makashima, 1979). Nevertheless, the ef-fect of fibre orientation on pore size distribution is foundto be similar to that of the reported work by Kim andPourdeyhimi (2000) and Velu et al. (2004). It has also beendemonstrated that pore size significantly decreases with anincrease in fibre volume fraction and the number of layers.

AcknowledgementsThe author expresses gratitude to Dr Vera Chetty of the Universityof Bolton and Mr Patrice Hellebaut of Benelux Scientific forsupplying and measuring the pore size distributions of nonwovens,respectively.

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1.Structural Analysis of Pore Size Distribution of Nonwovens

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