The research of physical-mechanical characteristics of ecological thermal insulation/Ekologiskos termoizoliacijos fiziniu-mechaniniu savybiu tyrimai.
Janulaitis, T. ; Paulauskas, L. ; Eidukynas, V. 等
1. Introduction
One of the most developing industrial brands in the world is the
manufacturing of ecological and eco-friendly building materials.
Ecological and eco-friendly materials are manufactured from secondary
processing refuse or from shifting reservoir without the supplement
adverse to health or environment. The manufacture of this type materials
is related to environment protection, green-house effect decrease and
energy saving. Materials should have small thermal conduction and
adequate strength characteristics in order to be used in building
enclosures in which different short-term and long-term loads act and
which could compete with nonecological contemporary effective thermal
insulations.
Ecological organic thermal insulation slab is manufactured using
secondary raw materials or cardboard and synthetic fiber polyolefin as a
binding material [1]. In order to determine thermodynamical and
mechanical characteristics of formed materials, the samples of material
with different amount of binder, different structure, different
densities and using 3 different granulometric composition cardboard
milling refuse were prepared.
2. Experimental part
2.1. The research of thermal conduction
The heat release is a very complicated physical process because it
depends on hydrodynamical and thermal processes. The heat amount that
body surface gets or releases during a time unit while enacted by mass
stream is proportional to the difference of temperatures of the body
surface and mass stream. Newton described this law by using the
following equation
Q = kS[increment of T] (1)
here S is surface area, [m.sup.2]; [increment of T] is the
difference of mass stream and surface temperatures. If mass stream
temperature is higher than surface temperature, so [increment of T] = T
- [T.sub.s], here T and [T.sub.s] are the average temperatures of mass
stream and surface [2].
Thermal conduction coefficient is a complicated function of many
variables. Thermal conduction coefficient can be determined notionally
or during the experiment. In the first case it is calculated from
differential equations, which describe hydrodynamical and thermal
processes. These processes happen when mass stream affects the surface.
However, these equations are complicated, many assumptions are made
while resolving them. That is why the results are not always correct. In
the second case it is determined from experimental research of thermal
transfer.
In order to determine thermal conduction coefficient we have used a
constant thermal flow method. Thermal conduction coefficient [lambda] is
determined while measuring thermal flow and the difference of
temperature when sample geometry is known. The experiments were carried
out in average 10[degrees]C temperature, using (300 x 300 mm) samples,
which were (40-50 mm) thick. Prior to the experiment the samples were
kept at least for 6 hours in 23 [+ or -] 5[degrees]C temperature and 50
[+ or -] 5% relative air humidity environment. The prepared samples were
put in the thermal conduction determination device and were squeezed
with 50 Pa load. When thermal flow and temperatures in both sides of the
samples are steady, the experiment is complete. Experiment lasts for 2-3
hours.
The Fox 304 device (manufacturer-Laser Company, USA) was used to
measure thermal conduction. The device is computerised, final
experimental results are computed by program LaserComp.
2.1.1. The research of binder, density and structure impact on
material thermal conduction
The values of binder, thermal conduction, density and granulometric
composition that were measured during the experiment are given in Table
1.
As it can be seen from the data given in Table 1, average density
of the samples varied from 78.6 kg/[m.sup.3] to 123 kg/[m.sup.3]. The
biggest density have the samples prepared using milled nonbolted
cardboard, the amount of binder was 10% from milled cardboard amount.
The smallest density had the samples prepared using 5% binder amount and
fine (1.5 mm) and rough (5.0 mm) milled cardboard fraction.
As results from the Table 1 show, the amount of binder influence
sample density. The sample density with bigger binder amount is about
10% bigger. When binder amount in all the samples is constant, that is
5%, the biggest influence on thermal conduction makes granulometric
composition. The smallest thermal conductivity have the samples prepared
using milled cardboard, which was bolted through 1.5 mm bolter. All
values of all the samples are given in Fig. 1.
[FIGURE 1 OMITTED]
As it can be seen from Fig. 1, the results of samples are
chaotically scattered. It shows that thermal conduction is influenced by
several parameters.
In Fig. 2 the reliance of thermal conduction from two
parameters--density and the amount of binder are shown.
[FIGURE 2 OMITTED]
According to experimental data thermal conduction reliance from
density and amount of the binder is described by the empirical equation
[lambda] = [0.0540 + [[rho].sup.-1.304]]/-0.466 [exp.sup.(-(0.0317
Z))] (2)
with coefficient of determination [R.sup.2] = 0.74589 and average
standard deviation S = 0.00067463; here Z is amount of the binder in %
from milled cardboard mass [3].
Determination coefficient shows if there is a stochastic relation
between y and all analysed factors. Determination coefficient is equal
to the square of correlation coefficient and it shows how many percent
of the analysed factor values are explained by the regression equation.
Whereas three parameters--density, amount of binder and milled
cardboard granulometry were changed, we got empirical thermal conduction
reliance from all three parameters.
Thermal conduction reliance on density, amount of binder and
granulometric composition
[lambda] = [-0.9890 + [[rho].sup.0.00834]]/(1 - (0.002220
[exp.sup.(-(-0.0503 Z)+(0.0255 G))])) (3)
with average standard deviation S = 0.02692 and determination
coefficient [R.sup.2] = 0.7434, here G is milled cardboard granulometry,
mm [3].
As we can see from Table 1, the smallest thermal conduction have
the samples with smallest density. As it is known from traditional
materials research, in small density materials convection proceeds
intensively--heat is freely transported between separate thermal
insulation elements--in fibrous materials between separate fibres, in
foam--between separate granules and etc. When a particular density in
the material structure is reached, big cavities deplete, accordingly
depletes convection. When the material density is enhanced further on,
thermal conduction increases again because of the bigger thermal
conduction through solid material carcass [3]. Thanks to the binder we
get products of this density while forming ecological material. Because
heat transfer during the convection is small, that is why in all cases
when density is enhanced thermal conduction encreases because of the
heat transfer through solid material carcass. Thermal conduction in this
material encreases as well because of the bigger amount of contacts
between separate elements and better contact between them.
In all cases granulometric composition of milled cardboard refuse
used for samples had an impact to thermal conduction. First of all,
cardboard granulometric composition decided density of the samples. The
smallest thermal conduction and density had the samples prepared using
the smallest granulmetric cardboard grist--1.5 mm and smaller. Meanwhile
the samples, prepared using non-bolted and bolted through 5 mm bolter
cardboard grist, were almost identical in density as well as in thermal
conduction. Fine fraction samples distinguished in more homogenous
structure and smaller cavities between separate elements. Smaller
cardboard fraction was more ruffle, that is why we got smaller sample
density.
However, we could not decrease granulometry and sample density
more, because the samples are formed from two elements--filler, which
consists of cardboard grist and binder, which consists of polypropylene
refuse. When smaller fraction is used and it is tried to bind it with
polyolefin binder, the smallest parts of cardboard begin to carbonize
because the smallest elements get hot quickly and polyolefin does not
reach the melting temperature.
The impact of the binder on thermal conduction and sample density
is significant. Comparing prepared sample with 5% and 10% of the binder,
it can be observed almost 20% sample density enhancement with the bigger
binder amount. When 10% the of binder is used, polypropylene not only
binds separate cardboard particles, but also it's separate fibres
melt and thicken. Samples prepared with the amount lower than 5% of the
binder were weak and they hardly kept their form, that is why they were
not used in further research.
2.2. The research of strength indicators
The most important strenght indicators of thermal insulation
materials are: compression stress by 10% deformation, stress parallel to
the sample surface and compressibility [4]. For compression research
there were 3 samples with different amount of the binder and different
granulometry prepared. Sample measurements were 100 x 100 x 40 mm. The
samples were measured and weighed, then they were put on raft of
compression device. The sample was laid on the centre of upper moving
board and it was pressed with constant 0.1 d speed per minute and not
bigger than 25% declination (here d--sample thickness in mm). The
accuracy of strenght scan--0.1 N.
In order to determine if a material has sufficient force by
stretching in order it could withstand loads which form while
transporting and installing it, a research of parallel with surface
strenght by stretching [[sigma].sub.t] according to standard LST EN 1608
is exercised. The product is presuposed to be appropriate for usage in
enclosuring constructions if it withstands the stress load equal to half
bigger mass of the product [5]. Three samples with different amount of
the binder and cardboard granulometry were prepared, their measurements
were 240 x 100 x 40 mm. Prepared sample is fixed in stretch device and
it is stretched with constant 10 x (1[+ or -]10%) mm/min speed, untill
it collapses. The biggest stress force is recorded.
Parallel with surface stress is calculated according to the formula
[[sigma].sub.mt] = [F.sub.m]/d b (4)
here [F.sub.m] is the highest stress force, kN; d is sample
thickness, m; b is sample width, m [5].
Compression research is performed in order to choose floor payload
for floating floor construction. Prepared sample, 100 x 100 x 40 mm, is
put in computerized press on the centre of moving board. It is pressed
with constant speed according to the set pressing scheme [6].
Hounsfield HQ10 (England) universal experiment device was used for
research.
2.2.1. Determination of compression stress by 10% deformation
For compression stress evaluation the samples with different amount
of the binder and different cardboard granulometry composition were
prepared. Results of the research are presented in Table 2.
Most often strenght indicators of thermal insulation materials are
asociated with density of the material [7]. In Fig. 3 compression
dependance of materials on density is presented.
In Fig. 3 the items of each party are indicated by different signs.
Even samples of the same composition (granulometry and amount of binder)
have a big range of results. The smallest dispersion of the results is
noticed in the samples were 1.5 mm granulometry cardboard particles and
5% of the binder is used. It is possible to state that thanks to small
cardboard particles the raw materials are mixed better and more
homogenuous structure of a sample is obtained. As we can see from Table
2, if the amount of the binder is upgraded two times, that is from 5% to
10%, the compression stress also upgrades about two times. However, the
bigger amount of the binder spreads not so good in formed item and other
characteristics of the material worsen as well.
[FIGURE 3 OMITTED]
As for many other building materials the granulometric composition
of cardboard influence compression stress a lot [8]. As the data
analysis shows, the samples with the same amount of the binder but with
different granulometry have compression stress that can differ four
times or more. The biggest force have the samples prepared with
nonbolted cardboard, that is from different sizes of particles, that
spread evenly in matrix of the item and form more contact zones that
enable items to stand the load. The samples, made with 5 mm fraction
cardboard have more than two times smaller force. In this case the
binder fills big spaces between fill parts, so the binding material
spreads worser in all matrix of sample and because of that there is less
contact zones supporting separate particles.
A very small compression stress is obtained by using 1.5 mm
fraction fills. Although the binder spreads evenly in sample matrix, but
the big part of cardboard particles does not contact with each other.
The particles are just hanging on the binder. Because of that a smaller
density of the samples than with other fractions is got and there is a
lot of free space for separate cardboard particles to move. In this case
the compression stress of polyolefin fibres affected not only by
compression but also by bending and extension stretches, but not the
cardboard particles, is determined. In order to determine the influence
of separate indicators to compression stress the dependance of
compression stress on density and amount of the binder was assessed,
Fig. 4.
[FIGURE 4 OMITTED]
According to experimental data empirical dependance of compression
stress from the amount of the binder is described by the following
formula
[[sigma].sub.10] = [11.13 + [[rho].sup.-0.0817]]/-0.0431
[exp.sup.(-(-0.106 Z))] (5)
with average standard deviation S = 1.130688 and coefficient of
determination [R.sup.2] = 0.73262.
Compression stress dependance on density, amount of the binder and
granulometric composition is described by the regression equation
[[sigma].sub.10] = [-18.0 + [[rho].sup.0.661]]/[(1 - (0.00127
[exp.sup.(-(-0.406 Z)+(10.94 G))]] (6)
with average standard deviation S = 1.056226 and coefficient of
determination [R.sup.2] = 0.82185 [4, 7].
2.2.2. The determination of stress parallel with sample surface
The research results of stress parallel with sample surface are
given in Table 3.
Comparing samples with the same granulometry (with nonbolted
cardboard particles) but with different amount of the binder a big
difference of results is observed. Two times bigger amount of the binder
exaggerates compression stress almost three times. The exact comparison
is disturbed by big dispersion of the results. The discrete results of
the samples prepared with 5% of the binder differ more than four times,
with 10% of the binder--more than two times, with 7.5% of the
binder--only 15%. These results show that amount of the binder does not
influence dispersion of the results. Dispersion of the results is
influenced by mixture of raw materials, regime of temperature and
different fraction amount of cardboard particles. Using nonbolted
cardboard particles different fraction amount stays changeable and
depends on bolting time, structure of initial raw materials,
composition, thickness, humidity and etc.
The results of samples with the same amount of the binder but with
different granulometry also differ a lot. The highest strenght have the
samples prepared from 5 mm fraction cardboard. This shows that bigger
cardboard fraction binds better with the binder. Furthermore, the binder
coats cardboard particles with thicker layer. Hereby bigger amount of
polypropylene fibres contact with separate cardboard particles. When
smaller fraction is used--1.5 mm, a very small stress is obtained. It
shows that separate poly-propylene fibres do not stipulate proper
fastening of cardboard fibres. Moreover, as it was mentioned in previous
chapters, smaller cardboard particles are "roughened" more, in
that way the initial structure of cardboard is destroyed. This is
approved by small density of the material. Comparing densities of the
samples prepared of 1.5 mm and 5.0 fractions, in all cases density of
the samples prepared of 1.5 mm fraction is by 20% lower than in the
samples, prepared of 5.0 mm fraction.
According to research results stress dependance on two
indicators--density and amount of the binder is shown in Fig. 5.
[FIGURE 5 OMITTED]
According to experimental data we got empirical dependance between
stress and amount of the binder
[[sigma].sub.t] = [-205 + [[rho].sup.-0.673]]/0.975
[exp.sup.(-0.0967 Z)] (7)
with average standard deviation S = 2.633303 and coefficent of
determination [R.sup.2] = 0.82060.
Stress dependance on density, amount of the binder and
granulometric composition is presented by the regressive equation:
[[sigma].sub.t] = [-116 + [[rho].sup.1.085]]/[1 - (0.00843
[exp.sup.(-(-0.228Z)+(21.83G))]] (8)
with average standard deviation S = 2.82327 and coefficient of
determination [R.sup.2] = 0.79259 [5, 7].
2.2.3. Determination of compressibility
The results of compressibility research of prepared samples are
given in Table 4.
While executing compressibility research the samples were treated
in different periods of time and with different values of compression
loads [6]. However, the bigger inluence for the samples had the duration
of load. Additionally, the results are presented not in stress values,
but in shift depletion after the load was removed in Fig 6.
As we can see from the data, presented in Table 4, the biggest
influence on compressibility of the material has amount of the binder.
Sample compressibility is lower with bigger amount of the binder, it
means that the sample recovers its form better after the load is
removed. It is conditioned by bigger numbers of connections between the
binder and cardboard fibres.
[FIGURE 6 OMITTED]
According to experimental data we got empirical dependance between
compressibility and amount of the binder
C = [0.798 + [[rho].sup.-0.552]]/0.00272 [exp.sup.(-(-0.140 Z))]
(9)
with average standard deviation S =1 654157 and coefficient of
determination [R.sup.2] = 0 98584 .
Stress dependance on density, amount of the binder and
granulometric composition is presented by the regressive equation
C = [-2.59 + [[rho].sup.0.520]]/(1 - (0.515
[exp.sup.((-0.188Z)-(0.641G))])) (10)
with average standard deviation S = 0.766202 and coefficient of
determination [R.sup.2] = 0.98584 .
3. Conclusions
1. During the new generation thermal and mechanical experiment it
was established that the biggest density have the samples prepared with
nonbolted milled cardboard and amount of the binder is 10% from shredded
cardboard amount are also increased.
2. When amount of the binder is increased, density of the material
and the value of thermal conduction coefficient.
3. The granulometric composition of cardboard decided the density
of the samples. The lowest thermal conduction and density had the
samples with the smallest granulometric cardboard fibres--1.5 mm and
smaller.
4. During the mechanical and thermodynamical characteristics
research it was determined that to form ecological thermal insulation
slab 5% amount of the binder is sufficient.
Received April 21, 2011
Accepted April 05, 2012
References
[1.] Janulaitis, T.; Paulauskas, L. 2009. Manufacture parameters of
thermal insulation slabs from secondary raw materials, Mechanika 6(80):
72-76.
[2.] Strazdas, K.; Eidukevicius, J. 1985. Mineral and Glass Fibre,
Vilnius, Mokslas, 151p. [in Lithuanian].
[3.] Bliudzius, R.; Samajauskas, R. 2001. The peculiarities of
determination of thermal conduction coefficient of light fibruous,
Medziagotyra. 7(4): 280-284.
[4.] Buska, A.; Maciulaitis, R. 2007. The compressive strength
properties of mineral wool slabs: influence of structure anisotropy and
methodical factors, Journal of Civil Engineering And Management 13(2):
97-106.
[5.] LST EN 1608+AC:2000. Thermal insulating products for building
applications--Determination of tensile strength parallel to faces. 2000,
91.120.10. 5 p.
[6.] LST EN 12431:2000. Thermal insulating products for building
applications--Determination of thickness for floating floor insulating
products. 2000, 91.100.99. 6 p.
[7.] Gailius, A.; Vejelis, S. 2009. Thermal Insulating Materials
and their Products. Vilnius: Technika. 247p. [in Lithuanian].
[8.] Kibirkstis, E.; Kabelkaite, A. 2006. Research of paperboard
mechanical characteristics, Mechanika 3(59): 34-41.
T. Janulaitis, Kaunas University of Technology, A. Mickeviciaus 37,
44244 Kaunas, Lithuania, E-mail: tadas.janulaitis@gmail.com
L. Paulauskas, Kaunas University of Technology, A. Mickeviciaus 37,
44244 Kaunas, Lithuania, E-mail: lionginas.paulauskas@ktu.lt
V. Eidukynas, Kaunas University of Technology, A. Mickeviciaus 37,
44244 Kaunas, Lithuania, E-mail: valdas.eidukynas@ktu.lt
A. Balcius, Kaunas University of Technology, A. Mickeviciaus 37,
44244 Kaunas, Lithuania, E-mail: algimantas.balcius@ktu.lt
http://dx.doi.org/ 10.5755/j01.mech.18.2.1568
Table 1
The data of samples prepared for thermal conduction
Sample Sample Sample The Density, Thermal
party Nr. granulometry amount of kg/ conduction
binder, % [m.sup.3] coefficient,
W/(mK)
1 1 Non-bolted 10 101 0.0464
2 10 117 0.0486
3 10 123 0.0485
2 4 5 84.2 0.0473
5 5 94.2 0.0473
6 5 111 0.0481
3 7 1.5 mm 5 78.6 0.0449
8 5 84.2 0.0454
9 5 92.4 0.0470
4 10 5.0 mm 5 84.5 0.0471
11 5 91.9 0.0473
12 5 103 0.0480
Table 2
Data of samples prepared for compression experiment
Sample Sample Granulometric Amount Density,
party Nr. composition of binder, kg/[m.sup.3]
%
1 1 Nonbolted 5 97.0
2 105
3 93.9
2 4 Nonbolted 7.5 102
5 105
6 102
3 7 Nonbolted 10 103
8 107
9 107
4 10 1.5 5 84.9
11 85.1
12 79.7
5 13 5.0 5 102
14 104
15 99.8
Sample Compression
party stress,
kPa
1 2.42
2.50
2.71
2 3.54
5.62
4.60
3 4.81
4.31
7.09
4 0.571
0.622
0.519
5 1.58
2.34
1.61
Table 3
Data of samples prepared for extension research
Sample Sample Granulometric Amount of Density, Stress,
party Nr. composition binder, % kg/[m.sup.3] kPa
1 1 Nonbolted 5 89.4 19.1
2 87.0 4.26
3 88.7 11.65
2 4 Nonbolted 7.5 90.5 26.2
5 90.5 30.7
6 90.7 27.4
3 7 Nonbolted 10 90.7 17.5
8 93.2 41.5
9 92.9 32.4
4 10 1.5 mm 5 77.7 1.33
11 81.4 1.15
12 80.2 1.40
5 13 5.0 mm 5 99.8 20.3
14 101 19.9
15 97.8 14.3
Table 4
Data of samples prepared for compressibility research
Sample Sample Granulometric Amount Density Compressibility
party Nr. composition of kg/ mm
binder, [m.sup.3]
%
1 1 Nonbolted 5 97.0 11.1
2 97.8 11.1
3 99.4 10.6
2 4 Nonbolted 7.5 90.4 9.76
5 90.2 9.98
6 90.5 8.48
3 7 Nonbolted 10 91.4 8.48
8 91.8 8.77
9 92.2 8.32
4 10 1.5 mm 5 81.4 18.7
11 79.8 17.8
12 79.9 17.6
5 13 5.0 mm 5 105 11.1
14 102 11.8
15 101 10.9
