A New Approach for Thermal Resistance Prediction of Different Composition Plain Socks in Wet State (Part 2)

Abstract

Socks’ comfort has vast implications in our everyday living. This importance increased when we have undergone an effort of low or high activity. It causes the perspiration of our bodies at different rates. In this study, plain socks with different fiber composition were wetted to a saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in wet state at different moisture levels. Theoretical thermal resistance is predicted using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fiber) in different models. By this modification, these mathematical models can predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance has reason able correlation with experimental results in both dry (laboratory conditions moisture) and wet states.

Full text

A NEW APPROACH FOR THERMAL RESISTANCE PREDICTION OF DIFFERENT COMPOSITION PLAIN
SOCKS IN WET STATE (PART 2)
Tariq Mansoor*, Lubos Hes and Vladimir Bajzik
Faculty of Textile Engineering, Technical University of Liberec, Liberec, Czech Republic
*Corresponding author. Email: tariq.mansoor@tul.cz, taheembava1@gmail.com
1. Introduction
Consumers consider comfort as one of the most important
attributes in their purchase of apparel products; therefore,
companies tend to focus on the comfort of apparel products.
Comfort is a pleasant state of physiological, psychological, and
physical harmony between a human being and the environment
[1]. Clothing comfort has two main aspects that combine to
create a subjective perception of satisfactory performance:
thermo-physiological and sensorial. The thermo-physiological
relates to the way clothing safeguards and dissipates metabolic
heat and moisture [2,3], whereas the sensorial relates to the
interaction of clothing with the senses of the wearer [4,5].
Thermal-wet comfort being the strongest among tactile and
pressure comfort perceived by subjects during exercise [6].
Dry heat transfer occurs through conduction, radiation,
convection, and ventilation, whereas wet heat transfer when
sweating includes several additional complex processes
including evaporation, wicking, sorption and desorption, wet
conduction (additional conductive heat transfer due to the
clothing being wet), and condensation of moisture [7,8].
Thermal-wet comfort is mainly determined by the heat
and moisture transport of fabric, which is related to  ber
characteristics as well as yarn, fabric construction, and fabric
 nish, recognizing that the extent of their relationship to
comfort perception in clothing is also in uenced by garment
design, cut, and  t. The basic thermal comfort properties are
just two: thermal resistance (or insulation) and water vapor
resistance (or permeability) [8]. Increasing moisture content
in fabrics signi cantly worsens their ability to transport water
vapor. For wool fabrics and wool/viscose blended fabric, the
value decreases by over 70–80%. However, in the case of the
addition of polyester  bers, the effective permeability of water
vapor almost disappears, which is caused by substituting the
air in pores by water with higher thermal conductivity. This
means also that the physiological properties of the fabric,
which is becoming increasingly wet as a result of use, are
subject to sudden changes, which signi cantly affects the
quality of the apparel [9]. Oğlakcioğlu and Marmarali measured
the thermal resistance of cotton knitted fabric in a wet state.
Coolmax wetted fabric was used to simulate wetted skin. About
0.5 ml of water (containing detergent) was injected onto its
surface and waited for 1 min for the liquid had been uniformly
distributed within a circle of 45–50 mm. It was found that the
wetted fabrics indicate lower thermal insulation and cooler
feeling [10]. Clothing thermal insulation decreases during
perspiration, and the amount of reduction varies from 2 to 8%,
as related to water accumulation within clothing ensembles
[11]. Another study on footwear reported about 19–25% (30–
37% in toes) reduction of thermal insulation during sweating
[12]. Kuklane et al. measured the effect of different sweat rates
on thermal insulation and found a strong negative correlation.
Furthermore, they found that 30% of the total moisture can
stay in socks [13]. Thermal manikin results of dry and wet
heat loss are presented from different laboratories for a range
of two-layer clothing with similar dry insulations but different
water vapor permeabilities and absorptive properties. For
each climate, total wet heat loss is predominately dependent
on the permeability of the outer layer. At 10°C, the apparent
evaporative heat loss is remarkably higher than expected from
Abstract:
Socks’ comfort has vast implications in our everyday living. This importance increased when we have undergone
an effort of low or high activity. It causes the perspiration of our bodies at different rates. In this study, plain socks
with different  ber composition were wetted to a saturated level. Then after successive intervals of conditioning,
these socks are characterized by thermal resistance in wet state at different moisture levels. Theoretical thermal
resistance is predicted using combined  lling coef cients and thermal conductivity of wet polymers instead of dry
polymer ( ber) in different models. By this modi cation, these mathematical models can predict thermal resistance
at different moisture levels. Furthermore, predicted thermal resistance has reason able correlation with experimental
results in both dry (laboratory conditions moisture) and wet states.
Keywords:
Thermal resistance, plain socks, Mathematical models, wet state
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evaporation alone (measured at 34°C), which is attributed to
condensation within the clothing and increased conductivity of
the wet clothing layers [14]. The characterization of insulation
in wet states is very critical. There are many experimental
and prediction models available to fulll this need. Some
researchers employed articial neural networks (ANNs) models
for thermal resistance predictions [15,16]. Hes and Loghin
assumed thermal resistance of textile linked parallel to the
thermal resistance of water in their suggested mathematical
model [17]. Dias and Delkumburewatte’s mathematical model
predicted higher thermal conductivity than experimental [18]. In
the thermal resistance model of Matusiak, all the multilayered
fabric assemblies can be dened as cuboids lled with
randomly oriented innite cylinders (bers). Conductive heat
transfer can be calculated by analogy to electrical resistance
and Fricke’s law [19]. In most of the studies, thermal resistance
is predicted by statistical models [16,17]. Mangat et al.
presented a mathematical model for thermal resistance in the
wet state with the series and parallel combinations of air, ber,
and water resistance. Their predictions are in good correlation
with experiments by model-3 (air and ber resistance in series,
water in parallel) for denim fabrics while model-5 (Ra and Rw in
parallel arrangement and Rf in series) and model-7 (Rf and Rw
in serial arrangement and Ra in parallel arrangement) for weft
knitted eece fabric of differential ber composition [18,19].
Hollies and Bogaty have suggested a parallel combination for
measuring the effective thermal conductivity of moisten fabric
by combining the volume fraction and thermal conductivity of
water and polymer [20]. Naka and Kamata suggested three
parameters (air, water, and polymer) model with the combination
of parallel and series arrangements [21]. The problem with
Mangat’s models that they assumed the lling coefcient or
conversely porosity as constant components. But they are
changed with the changing of moisture levels because water
has a different density. Their second assumption that the air is
replaced by water is also not correct because of even >200%
moisture content air still present in the fabric. A mathematical
model for thermal resistance prediction, suggested by Wei
et al. [22], is also very simple like Mangat’s model. But they
considered only ber and air resistances. They ignored the
water content. Their recommended model has ber and air
in series plus air in parallel. Hollies and Bogaty have ignored
the series arrangement and their calculation for water volume
presented in the fabric is also not clear. Naka and Kamata
suggested three parameters (air, water, and polymer) model
that was a good attempt but not conclusive, that is, use series,
parallel, or combination of both.
Although there are enough prediction models available for
different fabrics, these models are very complicated and limited
to dry states. So the present research aims to measure the
thermal resistance by different skin models and nd or develop
a simple mathematical model for thermal resistance prediction
based on available physical parameters especially in wet states
for socks with differential ber composition.
2. Material and methods
2.1. Materials
All the socks samples have been knitted on the same machine
(Lonati 144N 4’’) settings by varying the main yarns to get the
homogeneous samples with respect to specs and stretches
for contrast comparison. After knitting, all the samples were
processed for washing in the same machine bath followed by
tumble drying and boarding.
Table 1. Sock samples specications
Fiber composition (%) GSM
(g/m-2)
Thickness
(mm)
Fabric volumetric
density (kg/m-3)
Sock
codes
Cotton 80%, polyester 18.20%, and elastene 1.8% 276.45 1.080 255.95 P1
Viscose 81.08%, polyester 17.22%, and elastene 1.70% 373.98 1.06 352.82 P2
Polyester 98.38% and elastene 1.62% 252.03 0.96 262.53 P3
Nylon 70.83%, polyester 26.54%, and elastene 2.63% 227.64 1.09 208.85 P4
Polypropylene 65.22%, polyester 31.65%, and elastene 3.13% 211.38 1.04 204.23 P5
Wool 76.19%, polyester 21.67%, and elastene 2.14% 268.29 1.32 203.25 P6
Acrylic 81.25%, polyester 17.06%, and elastene 1.69% 390.24 1.55 251.77 P7
Figure 1. Plain sock construction.
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2.2. Methods
2.2.1. Alambeta
Thermal resistance (Rct) assessed using the Alambeta tester
[23], which enables fast measurement of both steady-state and
transient-state thermal properties. This instrument simulates,
to some extent, the heat  ow q (Wm−2) from the human skin to
the fabric during a short initial contact in the absence of body
movement and external wind  ow. Thermal resistance (Rct)
(m2KWˉ1) is used to express the heat insulation properties of
a fabric. Rct of textiles is affected by  ber conductivity, fabric
porosity, and fabric structure. It is also a function of fabric
thickness, as shown by the following expression:
(1)
2.3. Theoretical models
All the theoretical models for thermal resistance
prediction are used by feeding the wet  bre thermal
conductivity (
prediction are used by feeding the wet  bre thermal
) and  lling coef cient ( wet polymer
F
)
of wet polymer instead dry and amended accordingly except
Mangat’s model. wet polymer
F
and
of wet polymer instead dry and amended accordingly except
are calculated
as per Eqs (11)–(13). After this amendment, these models can
also predict thermal resistance for wet fabrics.
2.3.1. Fricke’s modi ed model [24]
Thermal conductivity of  brous material whose  bers are
perpendicular to the heat  ow can be determined by the
following equation:
(2)
λfab = Fabric thermal conductivity, λwet polymer = Wet  bre thermal
conductivity, λa = Air thermal conductivity, Fwet polymer = Fiber
 lling coef cient + Water  lling coef cient, and Fa = Air  lling
coef cient.
2.3.2. Ju Wie modi ed model
Wie et al. [22] have divided the fabric basic unit into three parts
in heat transfer  eld: part I is composed of solid  bers, part
II is the porosity vertical to the heat  ow direction, and part
III is the porosity parallel to the heat  ow direction, as shown
as Figure 2. Fabric thermal resistance depends largely on the
heat transfer process in the basic unit. In this model, heat  ow
considered through the fabric in a combination of  ber and air
in series plus air in parallel.
Figure 2. Ju Wie model diagram.
(3)
Rfabric = Fabric thermal resistance (m2KWˉ1), D = Fabric
thickness (m), λair = Air thermal conductivity (Wm-1K-1),
λwet polymer = wet  bre thermal conductivity (Wm-1K-1), a = Fabric
structural parameter=
compressed
D
D
, D = Thickness (m) measured
at 2 kPa pressure, while Dcompressed = Thickness (m) measured
at 15 kPa.
2.3.3. Maxwell–Eucken2 (ME2)’s modi ed model
Maxwell–Eucken (ME) model [25,26] (Eq. 4) can be used
to describe the effective thermal conductivity of a two-
component material with simple physical structures. In Eq. (4),
component material with simple physical structures. In Eq. (4),
are the thermal conductivities
and volume fractions, respectively, and subscripts representing
the two components of the system.
and volume fractions, respectively, and subscripts representing
is the effective thermal
conductivity of the two-component material. An emulsion is
a dispersion of one liquid in another immiscible liquid. The
phase that is present in the form of droplets is the dispersed
phase and the phase in which droplets are suspended is called
the continuous phase. Several effective thermal conductivity
models require the naming of a continuous and dispersed
phase. The materials with exterior porosity, individual solid
particles are surrounded by a gaseous matrix, and hence
the gaseous component forms the continuous phase and the
solid component forms the dispersed phase [27]. For external
porosity, and
solid component forms the dispersed phase [27]. For external
are considered as continuous
and dispersed phases, respectively.
(4)
wet polymer
F
and are calculated as per Eqs (11)–
(13).
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2.3.6. Suggested amendments and calculations
By assuming that fabric density is changing with wetting, which
causes to change the  lling coef cient, porosity, and thermal
conductivity of the fabrics. Based on these assumptions, the
following three equations are developed that will be used to  nd
the fabric density,  lling coef cient, and thermal conductivity
for different moisture levels. Average thermal conductivity for
different  bers (within socks) at different moisture levels will be
calculated as per Eq. (11):
(11)
Fw = Water  lling coef cient, F b 1 = First  ber  lling coef cient,
F b 2 = Second  ber  lling coef cient, F b 3 = Third  ber  lling
coef cient, λw = Water thermal conductivity, λ b 1 = First  ber
thermal conductivity, λ b 2 = Second  ber thermal conductivity,
and λ b 2 = Third  ber thermal conductivity.
The  lling coef cients for water,  ber, wet polymer, and air are
calculated as per below steps:
Air  lling coef cient (
a
F
) is calculated as per the following Eq.
(12):
(12)
Filling coef cient for wet polymer will be calculated as per Eq.
(13). This value will be used as input in all the above models for
the measurement of thermal resistance in wet states.
(13)
2.3.4. Schuhmeister’s modi ed model
Schuhmeister [28] summarized the relationship between
the thermal conductivity of fabric and the fabric structural
parameters by the following equations:
(5)
(6)
(7)
where is the thermal conductivity of fabric,
is the conductivity of wet  bers,
is the thermal conductivity of fabric,
is the conductivity of air,
wet polymer
F
is the  lling coef cient of the solid  ber, and
a
F
is
the  lling coef cient of air in the insulation.
2.3.5. Militky’s modi ed model
Militký and Becker [29] summarized the relationship between
the thermal conductivity of fabric and the fabric structural
parameters by an empirical equation:
(8)
(9)
(10)
where is the thermal conductivity of a fabric,
is the conductivity of wet  bers,
is the thermal conductivity of a fabric,
is the conductivity of air,
wet polymer
F
is the  lling coef cient of the solid  ber, and
a
F
is
the  lling coef cient of air in the insulation.
Table 2. Filling coef cients calculation
Measurement
=Water filling coefficient
w
F
=Fiber filling coefficient
fib
F
Content % %
Weight g g
Area m2 (Fabric) m2 (Fabric)
Areal density gm-2 gm-2
Volumetric density
Filling coef cient
Volumetric Density
Water Density
Volumetric Density
Fibre Density
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resumed their dry (lab conditions) Rct after 6 h of conditioning.
P2, P6, and P7 could not resume their thermal resistance even
after 8 h. P5 (polypropylene) has the highest moisture loss or
evaporation rate followed by P6 (wool) and P3 (polyester). P1
(cotton) and P2 (viscose) are the worst ones. P4 (nylon) and
P7 (acrylic) fallen in the middle. Predicted (by different models)
and experimental thermal resistance is given in Table 4. For all
the models, the input thermal conductivity and  lling coef cients
were measured for wet polymer at different moisture levels.
The correlation between experimental and predicted models is
checked by r2 value. The values of coef cient of determination
for all the models showed that these models can make
reasonable predictions of thermal resistance in dry as well as
The output of Eqs (11)–(13) is used as input in all the above
models. So with the combinations of suggested and above-
mentioned models, thermal resistance at different moisture
levels will be predicted. The thermal conductivity of water and
air is taken as 0.6 and 0.026 Wm-1K-1, respectively, while the
density of water is 1000 kgm-3. The values of the different input
parameters used in this study are given in Table 3 [30].
2.4. Statistical analysis
Theoretical and experimental results are statistically analyzed
by the coef cient of determination (R2) and the sum of squares
of deviation (SSD). Correlation graphs are drawn through
scatter diagrams in Microsoft excel. The following are the
equations behind the calculation of (R2) and SSD [31].
²
²² ²
xy
xy
s
Rss
= (14)
(15)
3. Results and discussion
Sock samples were tested for a relative cooling effect, thermal
resistance, and thermal absorptivity in the dry state (laboratory
conditions moisture content). Then, wet to saturated level (70%
moisture content) by BS EN ISO 105-X12 standard test method.
Establish technique for preparing wet fabric of a known oven-
dry weight of the fabric, then thoroughly wet out it in distilled
water. Bring the wet pick-up to 70 ± 5% by putting wet testing
fabric on a blotting paper. Avoid evaporative reduction of the
moisture content below the speci ed level before the tests
are run. Furthermore, tested again after 2, 4, 6, and 8 h of
conditioning successively in laboratory standard environmental
conditions at known moisture level.
3.1. Effect of moisture on thermal resistance (m²KW-1)
As mentioned earlier, dry and wet socks with differential
moisture content were checked on Alambeta. The Alambeta
is selected to avoid the effect of convection. Figures 3–11
demonstrated that as the moisture (%) increased thermal
resistance decreased and vice versa irrespective of sock  ber
composition or structure. Only P3, P4, and P5 socks have
Table 3. Different  bers’ properties
Fiber name Density (kgm-3)
Thermal
conductivity
(Wm-1K-1)
Cotton 1540 0.5
Viscose 1530 0.5
Polyester 1360 0.4
Nylon 66 1140 0.3
Polypropylene 900 0.2
Wool 1310 0.5
Acrylic 1150 0.3
Figure 3. Theoretical thermal resistance vs experimental at different
moisture levels (P1).
Figure 4. Theoretical thermal resistance vs experimental at different
moisture levels (P2).
Figure 5. Theoretical thermal resistance vs experimental at different
moisture levels (P3).
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in dry and wet states at all the moisture levels followed by Ju
Wie and Maxwell.
Fricke has overall top thermal resistance prediction generally
at 3%, 6%, 36%, and 52% moisture level specically for P4
(nylon 70%, polyester 26.54%, and elastene 2.63%) as shown
in Figure 6 followed by Ju Wie and Maxwell. Militký again got
the lowest position. Schuhmeister on the second number from
the lower side. This is also veried from SSD values, that is,
0.0000382, 0.0000797, 0.0000901, and 0.000404 for Fricke,
Ju Wie, Maxwell, Schuhmeister, and Militký, respectively.
In Figure 7 for P5 (polypropylene 65.22%, polyester 31.65%,
and elastene 3.13%) sock, Schuhmeister and Maxwell’s
prediction is the best among all other models with less SSD, that
is, 0.0000286325 and 0.000031 with respect to experimental
thermal resistance. Then, Ju Wie (SSD = 0.0000887) followed
by Militký (SSD = 0.000101) and Fricke (SSD = 0.000115).
Figure 8 shows the effect of moisture content (%) on the
thermal resistance of P6 sock (wool 76.19%, polyester
21.67%, and elastene 2.14%). P6 sock could not resume its
dry state moisture content and ultimately thermal resistance
after 8 h of conditioning due to its hydrophilic nature. 7.43%
moisture content is due to the presence of polyester ber in the
composition in the dry state. All the models have a reasonable
prediction of thermal resistance as evident in Figure 8.
the wet state also at different moisture levels for all the major
ber blends being used for socks.
The predicted and experimental thermal resistance of P1
(cotton 80%, polyester 18.20%, and elastene 1.8%) at various
moisture levels is given in Figure 3. Maxwell model has the
best prediction at 10.1%, 20.61%, 61.17%, and 67.02%
moisture levels followed by Frick, Schuhmeister Militký, and Ju
Wie. P1 sample has still about 21% moisture content after 8 h
of conditioning due to the higher composition of cotton ber
content (80%).
In the case of P2 sock (viscose 81.08%, polyester 17.22%,
and elastene 1.77%), the almost same trend is observed as
well as moisture loss is concerned after consecutive periods
of conditioning as shown in Figure 4. Maxwell and Fricke have
the best thermal resistance prediction at all moisture contents.
Schuhmeister and Militký have a good prediction at 43.83%,
57.88%, and 64.46% moisture contents. Militký has the best
prediction at 44%, 58%, and 64% moisture content. Ju Wie has
a better prediction where the moisture level is less than 20%.
P3 sock (polyester 98.38% and elastene 1.62%) has the
highest moisture loss (evaporation rate) due to polyester
hydrophobic nature after successive periods of conditioning as
shown in Figure 5. Militký’s model’s prediction is a bad one
among all the models. Overall, Fricke has the best prediction
Figure 6. Theoretical thermal resistance vs experimental at different
moisture levels (P4).
Figure 8. Theoretical thermal resistance vs experimental at different
moisture levels (P6).
Figure 7. Theoretical thermal resistance vs experimental at different
moisture levels (P5).
Figure 9. Theoretical thermal resistance vs experimental at different
moisture levels (P7).
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Figure 10. Coefcient of determination predicted vs experimental thermal resistance (m²KW-1).
Fricke has the lowest SSD (0.000229) followed by Maxwell
(0.000297), Ju Wie (0.000314), Schuhmeister (0.001136876),
and Militký (0.00171).
Figure 9 shows the effect of moisture content (%) on the thermal
resistance of P7 sock (acrylic 81.25%, polyester 17.06%, and
elastene 1.69%). All the models have a reasonable prediction
of thermal resistance as evident in Figure 11. Lesser the SSD,
better the prediction. Maxwell has the lowest SSD (0.0000517)
followed by Ju Wie (0.00134), Fricke (0.000135), Schuhmeister
(0.00032638), and Militký (0.000744).
Figure 10 shows the model wise coefcient of correlation
between theoretical and experimental thermal resistance
in dry/wet states by all the models for all the sock samples
irrespective of sock composition. Maxwell has the highest
correlation (R2 = 0.8559) followed by Fricke (R2 = 0.8312),
Ju Wie (R2 = 0.8224, Schuhmeister (R2 = 0.8079), and Militký
(R2 = 0.873).
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Table 4. Thermal resistance at various moisture levels (predicted vs experimental)
Sock code Moisture
content (%)
Thermal resistance (m2KWˉ1)
Fricke
modied
Ju Wie
modied
Maxwell
modied
Schuhmeister
modied
Militky
modied Experimental
P1
10.10 0.029075 0.026768 0.0259 0.017867 0.014374 0.0256
20.61 0.027054 0.025002 0.0236 0.015748 0.012458 0.0204
44.03 0.02069 0.020234 0.0172 0.010955 0.008389 0.0105
61.17 0.01311 0.015747 0.0104 0.007288 0.005534 0.0082
67.02 0.009509 0.013955 0.0075 0.005909 0.004538 0.0071
P2
6.62 0.025473 0.023683 0.0221 0.01491 0.011775 0.0275
20.22 0.022432 0.021359 0.0190 0.012422 0.009634 0.0208
43.83 0.015047 0.016653 0.0121 0.008206 0.006242 0.0101
57.88 0.008557 0.013343 0.0068 0.005574 0.004311 0.008
64.46 0.004649 0.011632 0.0038 0.003955 0.003246 0.0076
P3
0.58 0.026292 0.024315 0.0236 0.017934 0.014817 0.0304
0.70 0.026276 0.0243 0.0236 0.017911 0.014795 0.0277
1.39 0.026183 0.024212 0.0235 0.01778 0.014667 0.0271
34.26 0.020171 0.019273 0.0171 0.011545 0.009006 0.016
49.55 0.015646 0.01628 0.0128 0.008636 0.006615 0.0154
P4
2.57 0.030954 0.028764 0.0281 0.023101 0.019639 0.0346
6.03 0.030477 0.028291 0.0276 0.022303 0.018822 0.0339
19.67 0.028311 0.026266 0.0251 0.019125 0.015691 0.0232
36.99 0.024601 0.02319 0.0212 0.015009 0.011907 0.0223
52.27 0.019801 0.019791 0.0164 0.011276 0.008725 0.0183
P5
0.00 0.028914 0.027095 0.0264 0.023489 0.020484 0.0231
0.72 0.028821 0.027001 0.0263 0.023319 0.020302 0.0225
21.45 0.025759 0.024122 0.0228 0.018425 0.015291 0.0221
53.34 0.017837 0.01824 0.0148 0.010739 0.008376 0.0127
66.20 0.012062 0.014911 0.0097 0.007466 0.005771 0.0107
P6
4.24 0.038036 0.035071 0.0344 0.024769 0.020317 0.0488
11.46 0.036815 0.033891 0.0330 0.023166 0.018792 0.0408
17.99 0.035563 0.032729 0.0316 0.021677 0.017409 0.0353
43.62 0.028582 0.027029 0.0242 0.015439 0.011936 0.0202
54.13 0.024144 0.023973 0.0199 0.012662 0.009662 0.019
P7
3.53 0.040875 0.038067 0.0366 0.029739 0.02492 0.0428
7.01 0.040085 0.037346 0.0357 0.028579 0.023773 0.0339
22.11 0.036104 0.03396 0.0313 0.023589 0.01904 0.0329
40.37 0.029509 0.029102 0.0247 0.01762 0.013786 0.0223
51.56 0.023884 0.025541 0.0195 0.013954 0.010773 0.0183
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properties of cotton knitted fabrics in dry and wet states.
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4. Conclusion
By adopting the new approach of feeding wet polymer lling
coefcient and thermal conductivity instead of dry polymers,
different models can make a reasonable prediction of thermal
resistance in wet states as well. All the models have a coefcient
of determination (R2) >0.78.
Polymer lling coefcient remains constant while water and air
lling coefcients are changing with the variation of moisture
which leads to change the thermal conductivity.
P3, P4, and P5 socks samples have resumed their dry
(laboratory conditions) Rct after 6 h of conditioning. P1, P6,
and P7 could not resume their insulation even after 8 h of
conditioning.
This study was conducted after successive periods of intervals
to monitor the evaporation rate as well. So that many moisture
contents (%) point missed in the graphs for some samples. The
next study could be planned to test controlled moisture [32].
Acknowledgment
This work was funded by the Technical University of Liberec,
Czech Republic by SGS-2019 under project number 21314.
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