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Quantification of workmanship insulation defects and their impact on the thermal performance of building facades
Applied Energy 165 (2016) 272–284
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Quantification of workmanship insulation defects and their impact on the thermal performance of building facades A. Aïssani ⇑, A. Chateauneuf, J.-P. Fontaine, Ph. Audebert Blaise Pascal University, University of Clermont Ferrand, Pascal Institute, Clermont Ferrand, France CNRS, UMR 6602, Institut Pascal, F-63171 Aubière, France
h i g h l i g h t s Effective performance of insulations altered by practical defects is investigated. Analytical models evaluate the effective thermal conductivity to use in simulation. The energetic impact of common workmanship defects is studied. The presence of openings in facade walls are found to be more harmful.
a r t i c l e
i n f o
Article history: Received 14 September 2015 Received in revised form 27 November 2015 Accepted 13 December 2015 Available online 4 January 2016 Keywords: Insulation Effective thermal conductivity Workmanship defects Uncertainties
a b s t r a c t Nowadays, many performing insulation materials are available on the market. However, their expected thermal performance can be affected by many sources of uncertainty due to random errors that can occur during the manufacturing and the measurement processes. In addition, the thermal performance is strongly affected by another source of uncertainty related to the insulation laying process. As a matter of fact, defects in insulation panels are introduced either for practical reasons or due to a lack of rigor of workers. These errors are still not yet properly considered for simulation, although they result in significant heat losses. This work aims at investigating the impact of four common workmanship errors on the thermal performance of insulation panels. A coupling between experimental measurements and finite element modeling allows us to evaluate the effective thermal conductivity of insulations in presence of defects. The uncertainty analysis allows us to quantify the dispersion of insulation conductivity according to different types and sizes of defects, showing that flexible materials seem to be more affected by the defects. For simulation purpose, analytical models are proposed to assess the effective thermal conductivity in terms of size and type of defects. The numerical application to insulated wall shows the impact of each defect on the energy consumption, where deep grooves and openings are found to be strongly affecting the insulation performance. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The demand for energy is increasing worldwide because of increasing population and improving standards of living [1]. In the building sector, the greatest potential for energy savings is based on the reduction of heating and cooling [2], which helps to reduce building operating costs as well as emissions and other related impacts [3]. Therefore, more attention to the envelope insulation design is required, as it remains the first protection of ⇑ Corresponding author at: Blaise Pascal University, University of Clermont Ferrand, Pascal Institute, Clermont Ferrand, France. E-mail address: [email protected] (A. Aïssani). http://dx.doi.org/10.1016/j.apenergy.2015.12.040 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
the inner space from the outdoor climatic conditions. Physically, the effectiveness of the insulation depends on its thermal conductivity (k-value) and its ability to maintain its thermal characteristics over a large period of time [4]. In practice, to evaluate the coefficient of transmittance (U) of insulation, the k-values provided by manufacturers are used. However, This U-value is altered by other parameters, such as the presence of defects in the insulation materials themselves, as well as defects that occur during the installation procedures [5]. In the literature, several studies highlighted the uncertainty on insulation properties, due to the intrinsic variation of material properties, mankind errors, unavoidable measurement errors, random errors and non-representativeness of sample data, etc. [6,7].
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For instance, Dominguez-Munoz et al. [7] estimated the uncertainty that can be expected in the thermal conductivity of insulation materials for known and unknown material density. They finally provided a generic estimation of the uncertainties affecting the thermal conductivity of common insulation materials. For energy simulations, available models consider the deterministic thermal properties of materials, drawn from inspections and manufacturing measurements [6]. Moreover, several studies such as those of Aïssani et al. [8], Spitz et al. [9] and Hopfe and Hensen [10] considered these physical uncertainties in simulations to support decision making in Building Performance Simulation (BPS). However, in addition to the above mentioned material uncertainties, it is extremely important to consider the uncertainties associated to the weakest components of the envelope [11,12]. In insulated buildings, three kinds of defects can be observed 1. Design defects: these defects represent the Thermal Bridges (TBs) in building blocks. They generally occur at any junction between building components or where the structure composition changes [13–17]. 2. Implementation or workmanship defects: these defects are induced for practical reasons during retrofitting. They are most of the time due to the lack of rigor of workers, but also due to insufficient site controls. 3. Lifetime defects: these defects are due to aging, material settlement or moisture damage in building insulations. The presence of Design Defects (i.e. TBs), and implementation defects are the principal sources of heat losses in insulated buildings. Despite the fact that they are common rather than exceptional, their effects have been largely ignored in simulations although their presence reduces significantly the global thermal resistance of the envelope. As a consequence, the transmission loads increase and accordingly these defects jeopardize the beneficial use of thermal insulation [18], especially when high performance is required, such as in the case of low consumption buildings. In the literature, several studies have provided evidence that thermal performance of new dwellings can be significantly below the design predictions and investigated the scope for conducting thermographic inspections during the construction process to support the management of construction quality on sites [19–25]. Martin et al. [2] recognized that their impact is not yet properly calculated in building energy demand and as far as energy saving is concerned it can only be asserted that the proportion of the defect impact increases when the insulation quality of the envelope becomes higher. Up to now, most of scientists focus on TBs and lifetime degradations and most of their studies deal with thermal losses at junctions between two separately insulated elements, losses at openings or between vertical and horizontal elements [26,27] on one hand, while others deal with the lifetime defects due to cooling the interior surfaces and the resulting higher condensation which concerns molds and fungi growth [28,27], on the other hand. Regarding workmanship defects, it is still difficult to evaluate their exact impact on energy predictions as workmanship varies from a construction site to another. In practice, unlike other industries, the building industry cannot specify performance with zero allowances due to the variations of these workmanships [12]. In practice, many technical reports and scientific papers discuss workmanship anomalies [12] and most of them are aware that the observed deviation in performance of buildings is due to bad workmanship [12,29] in addition to other sources of uncertainty such as climate and efficiency of energy systems. However, with the emergence of the new energy requirements, it becomes important to consider these sources of uncertainty.
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In the literature, many works underlines the importance of considering these kinds of uncertainties for better performance predictions [30–32]. However, it is still difficult to consider these uncertainties because of lack of data [31], as almost no study properly addresses the quantitative impact of workmanship to consider in performance predictions. Workmanship anomalies are most of the time part of the building facade and can be a big source of heat losses. In this framework, the present study implementation defects that are due to shortcomings during the insulation laying. An experimental study is proposed to investigate usual irregularities induced by retrofitting. To do so, a coupling between the guarded heat plate method and thermography puts in evidence the heat flow losses induced by four types of defects (Fig. 1): Groove defect (EPS-G), which is generally found in homogenous and dense insulation materials. It consists in a regular and localized defect that appears when trying to make sheaths and electric wires passing through materials that cannot be crushed. Opening (MW-O), which is generally found between two insulation panels. This kind of defects is more noteworthy over time, as mineral wool collapses and openings become larger. As a matter of fact, this kind of defect may be considered as a special case of a groove-shaped defect, where the depth of the defect is equal to the total thickness of the insulation material. Crush defect (MW-C), which is the defect that is generally identified with flexible materials such as rock wool or glass wool. In contrast to the previous regular and localized defects, crushes make the geometry of the insulation material varies beyond the crush point. Sheath passage (MW-Sh). In this case, the presence of sheath in the insulation crushes partially the insulation panel, in addition to the presence of a gap around the sheath when it crosses the material. An experimental and numerical coupling has been carried out to develop analytical models representing the effective thermal conductivity of the insulation in presence of implementation defects. Finally, an application on an insulated external wall, including defects, is proposed to evaluate the energetic impact of these defects on the performance.
2. Experimental study To quantify the impact of implementation defects, an experimental work has been undertaken under laboratory conditions at the University Blaise Pascal, in France. The loss in thermal conductivity is assessed for each defect type and the uncertainty on the expected conductivity is then described by probabilistic distributions. The experimental set up (Fig. 2) seeks at determining the thermal resistance of insulation specimens by the mean of the Guarded Hot Plate, as presented in the standards ISO 8302 [33]. Each sample is standardized to 300 300 mm and at least 50 mm of thickness is adopted. The Guarded Hot Plate method allows the determination of the thermal resistance of homogenous and inhomogeneous specimens of parallelepiped forms. The test samples are sandwiched between hot and cold plates and a constant heat flow is imposed through the samples for a steady state transfer. To do so, it is important to achieve a quasi-stationary heat transfer state during the measurement with at least 2 or 3 h beforehand. Fig. 2 describes the apparatus used in this study. To determine the temperatures of the inner and outer sides of the specimens, temperature sensors are installed. On the inside face of the samples in contact to the
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EPS-G
MW-C
MW-Sh
MW-O
Fig. 1. Presentation of the different studied defects.
Fig. 2. Description of the apparatus used and its equivalent electric block diagram.
heating plate (guarded plate), a modular processing system is associated to k-type thermocouples to record heating temperatures (Tins). These data are then stored using VISULOG [34] traceability software. This tool allows the monitoring and the management of measurement channels of thermocouples. Over the measured
zone (Fig. 3), the thermocouples are distributed in order to have the best evaluation of the temperature field applied to the specimen. Fig. 4 shows the thermocouple locations. To determine the temperatures on the outer surface of the specimens (Tout), infrared thermography is used. The whole heat
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Fig. 3. Elements constituting the guarded hot plate apparatus.
heat losses at the specimen borders (Fig. 5). In addition, a thermohygrometer was used to determine the ambient atmospheric conditions i.e. ambient temperature and relative humidity. Fig. 6 gives a picture of the apparatus with and without the surrounding framework. To determine the resistance of the samples (R-value), it is assumed that the heat transfer from the wall to the thermal camera sensor is due to thermal radiation (Rad) and convection (conv) (Fig. 2), defined by [35]:
Q_ Rad ¼ 4 Q_ conv ¼ hIN
e r A T 3W ðT W T OUT Þ A ðT w T INS Þ
ð1Þ ð2Þ
where Q_ Rad and Q_ conv are the radiative and convective thermal heat flows [W] respectively, e is the thermal emissivity (0.94), r is the Stefan–Boltzmann constant, A is the section of the studied area [m2], hIN is the convective heat transfer coefficient [W/m2 K] for indoor environment, TW is the temperature of the wall [K], TOUT is the reflected outer area temperature [K] given by the thermal camera and TINS is the inner area temperature [K]. The thermal resistance R is then obtained from the total heat flow Q_ : Fig. 4. Thermocouples position at the measurement zone.
transfer processes of the thermography analysis, is sketched and detailed in the works of Fokaides and Kalogirou [35] and Asdrubali et al. [11]. For our testing procedure, an IR-camera manufactured by FLIR [36] (silver model with 320 256 pixel resolution) allowed us to record the different infrared radiations that are emitted by the specimens. Each image gives the temperature of each pixel hit by the radiation emitted from the tested object, defining the entire thermal field of the area covered by the camera optic [11]. The reflective properties of the surface itself can be corrected by applying masking or flat black painting or tapes to the surface before testing [36,35]. In this study, to have a good overview of the heat losses, flat black paint was applied on samples. For experimental purposes, a surrounding structure made of mineral wool has been set up to reduce convective and conductive
A Q_ ¼ ðT INS T OUT Þ R
ð3Þ
ðT INS T OUT Þ i R¼h ðhINS ðT W T INS ÞÞ þ 4erT 3W ðT W T OUT Þ
ð4Þ
Fig. 7 depicts the temperatures captured by the IR-Camera at the outer surface of the specimens shown in Fig. 1. Obviously, the highest temperatures are found at the defects locations, which indicate higher heat losses at these points. 3. Experimental and numerical coupling To determine the effective thermal conductivity of the specimens, a coupling between experimental results and numerical modeling is carried out. Fig. 9 depicts the procedure adopted for the assessment of this equivalent thermal conductivity:
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on the guarded hot plate tests, in order to determine the effective thermal conductivity of the for different defect sizes and insulation thicknesses. For each configuration, the number of tested specimens is between four and six. All of them come from the same manufacturer with the same density, thickness and thermal characteristics. The results are given in Table 1. In the case of EPS-G, the results are presented for different groove widths wd and depths td. The results show clearly the impact of the groove dimensions on the thermal conductivity of the insulation component. More specifically, it is observed that:
Fig. 5. Surrounding framework to reduce edge effects.
– First, all the defects are modeled using the Finite Element Method (FEM), using, Ansys software [37]. Although a regular mesh can be adopted for regular defects (grooves and openings), a tetrahedral mesh is considered for complex defects (crushes and Sheath passages) in order to allow for more accurate representation of the defect geometry (Fig. 8). The finite element analysis is applied by using experimental boundary conditions where a steady state transfer is considered. The specimen’s lateral borders are subjected to zero flux boundary conditions. The temperature field measured during testing (TINS) is applied on the inner surface of the finite element model and convective boundary conditions are applied on the outer surface. – To deduce the effective thermal conductivity, noted k⁄, of the insulation in presence of defects, the Finite Element (FE) analysis is performed for different thermal conductivities k and the resulting outer surface temperatures (Tout-FE) are compared to those observed experimentally (Tout-IR). An optimization procedure is then applied to define the effective conductivity k⁄ that better fits the experimental results, by minimizing the squared difference between the experimental and numerical temperature fields (i.e. inverse problem solution).
4. Effective thermal conductivity The effective thermal conductivity k⁄ is defined herein as the conductivity to adopt in case of defective insulation materials. The procedure described in the above section is now applied
1. The thermal conductivity losses increase significantly with the groove volume. Indeed, the average value of the obtained thermal conductivity increases when the groove width or depth increase. 2. The aspect ratio of the defect is also crucial. In fact, the thermal conductivity is more sensitive to deeper grooves td than to wider grooves wd. 3. The conductivity scatter is higher for small groove widths, and for high groove depths. This is emphasized by the fact that the thermal conductivity is more sensitive to the depth of the groove. Indeed, the affected area is larger with wide defects but losses are quasi-homogeneous over the affected area; whereas deep defects affect a smaller area of the material that makes high fluctuations of the thermal resistance between the affected area and the rest of the insulation, resulting in more dispersed results. In the case of MW-O defects, it is seen that the effective thermal conductivity is obviously affected by the gap created between the insulation panels. This gap induces concentration of heat losses at the opening location (Fig. 7-MW-O) resulting in losses in the thermal performance. The results of Table 1 indicate that the average thermal conductivity of the samples is 11% higher than the expected conductivity of the mineral wool without defects, when considering an opening of only 5 mm. However, the variability in experimental results does not exceed 2% due to the fact that the studied openings are very small. Nevertheless, over time the opening tends to be higher because of the aging and the collapse of materials, leading to higher heat losses and thus higher variabilities in the same way as wide grooves. In addition, higher dispersion is expected as mineral wool specimens are naturally flexible that makes each specimen more or less affected by the defect. In the case of MW-Crush defects, it is important to note that results do not consider the thermal resistance of wooden board on which the material is crushed and results present only the effective thermal conductivity of the crushed mineral wool. Obviously,
Fig. 6. Picture of the apparatus used in the laboratory; (a) measure zone without the surrounding framework; (b) measure zone surrounded by the mineral wool framework.
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EPS -G
MW -O
MW -C
MW -Sh
Fig. 7. Finite-Element meshes of the insulation panels with different types of defects.
EPS-G
MW-O
MW-C
MW-Sh
Fig. 8. Outside temperatures (Tout-IR) of the defective specimens by the IR-Camera.
Fig. 9. Effective thermal conductivity assessment procedure.
conductivities are found to be strongly altered by the crush level. As a matter of fact, when the crush is deep, the affected area around the crush point becomes wider, resulting in higher thermal losses. Moreover, the variability in testing results is found to be higher in this kind of defect. Besides, this variability obviously increases when the degree of crush is higher. Finally, the last studied defect is the MW-Sh defects, which could be seen as an extension of the crush defect, as the material is partially crushed. However, the presence of passage hole creates an extra noticeable losses even with the fact that the hole is partially filled by the sheath. The results presented in Table 1 correspond to MW specimens of 5 cm thicknesses and crossed by sheaths of 2 cm diameter. The effective thermal conductivity seems to be affected as much as the case of crushes. From Table 1, it can be seen that the average thermal conductivity obtained for MW-Sh is the same as the one obtained for MW-C in the case of a 2 cm defect. However, the coefficient of variation of the testing results is higher for crushes as the material is geometrically more affected.
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Table 1 Effective conductivity for tested specimens with different types of defects.
5. Probability distribution of the thermal conductivity In order to investigate the experimental distributions of the thermal conductivity, the median rank method is applied, usually recommended for small size of samples. In the case of EPS-Groove defect, Fig. 10a and b compare the experimental conductivity distributions to the normal and lognormal Cumulative Distribution Functions (CDF). The observation of the graphs in Fig. 10 shows that both Normal and Lognormal distributions seem to fit properly the experimental distributions. However, the goodness-of-fit test (Kolmogorov–Smirnov) allows us to consider that the lognormal distribution is more suitable. In all presented case studies of grooves, the hypothesis that the samples are drawn from normal and lognormal distributions is not rejected but the p-value1 returned by the test provides a decision on the best distribution. For all the studied EPS-G defects, the p-values were higher with the lognormal distribution, which allow us to recommend this distribution. Regarding the MW-Opening defect, Fig. 11 compares the CDF obtained with the median rank method to the normal and lognormal CDF. The observed uncertainty on the results is not significant. Although both normal and lognormal distributions seem to match the empirical CDF, the lognormal is preferred as the conductivity is a non-negative property. In contrast to previous regular and localized defects, crushes make the geometry of the material affected beyond the crush point. The final shape of the insulation in presence of crushes differs from a specimen to another due to the flexible and non homogeneous nature of mineral wool. This observation is reflected on the variability of conductivity values as shown in Table 1 and Fig. 12. Fig. 12a and b shows respectively the cases of 5 cm and 8 cm MW specimens crushed with 2 cm thick, and Fig. 12c shows the case of 8 cm MW specimens crushed with 4 cm thick. Experimentally, it has been noticed that, obviously large crushes make the affected area around the crush larger, resulting in an increase in the scatter of results. Both normal and lognormal distributions seem to fit the empirical CDF obtained as shown in Fig. 12a. However, only the lognormal distribution stands out and seems to better fit the experimental results in Fig. 12b and c. In the case of MW-Sheath defect, the impact of the passage hole makes the conductivities higher than a simply crushed insulation material. Indeed, it is clearly noticed that the effective 1 p-value is a value provided by the statistical tests. In general, statistical tests are used to test the validity of a claim (called null hypothesis) that is made on a statistical data set. The p-value can be interpreted in terms of a hypothetical repetition of the study. For instance, suppose the null hypothesis is true and a new data set is obtained independently of the first data set but using the same sampling procedure. The pvalue is the probability to obtain with the new data set the same decision of the test statistic as the original value.
conductivities of a simple crush of a 5 cm MW on a 2 cm wooden board (Fig. 12a) are better than the ones obtained in Fig. 13. Representing the MW (5 cm) crossed by a sheath of 2 cm diameter. Table 2 summarizes the adopted probability distributions and parameters which better describe the variability of the studied samples. In all cases, the hypothesis that the samples are drawn from normal and lognormal distributions was not rejected. From this table, it can be seen that results are accurate as the coefficient of variation is small. The variability associated to conductivity results is due to various measurement errors (random and epistemic) but also to the nature of the used material. As expected, uncertainties are higher using mineral wool specimens. These results give an idea about the scatter of the conductivity according to the defect considered. The advantage of describing variabilities using distribution laws help to support decision making process in building energy simulations. 6. Effective conductivity models for insulation with defects In this section, analytical models are proposed for BPS purposes. These models enable the assessment of the effective thermal conductivity to adopt according to the defect type and size. The first two defect types (i.e. groove and opening) can be considered as similar kinds of defects, as the groove and the opening are regular and localized defects. For these two types of defects, an analytical model is proposed for the assessment of the effective thermal conductivity to describe the insulation panel behavior in presence of such type of defects:
k ¼ k0 exp ½f ðt d ; wd Þ
ð5Þ
8 h i < f ðt d ; wd Þ ¼ a1 td 3 wd 2 þ b td 2 wd ða2 wd þ b Þ Groov e ð1Þ 1 t 2 G t w w w : f ðt ; w Þ ¼ a wd with t ¼ t Opening d d O d t d
ð2Þ ð6Þ
where k⁄ is the effective thermal conductivity of the material in presence of defects [W/m K], k0 is the thermal conductivity of the defect-free material (healthy material) [W/m K], fG and fO are the functions to use at the exponential power in Eq. (5) in case of groove-shaped and opening defects respectively, td is the defect depth [m], wd is the defect width [m], t is the thickness of the insulation panel [m] and w is the width of the insulation panel [m]. Both equations (Eqs. (6.1) and (6.2)) involve the geometrical dimensions of the defect and the size of the panel. In the case of grooves (Eq. (6.1)), we notice that whether the groove is deep or wide, the impact on the thermal performance is not the same. The impact in this case is more affected by the ratios (td/t) than (wd/w). The coefficients were then calibrated by using the experimental results, and may change from a material to another. In
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Fig. 10. Scatter of thermal conductivity in terms of groove width (a) and depth (b).
1
MW - O width = 5mm
0.9
Probabilies (%)
0.8 0.7 0.6 0.5 0.4 0.3
Median ranks
0.2
Lognormal Distribuon
0.1 0 0.035
Normal Distribuon 0.04
0.045
Experimental Conducvies (W/mK) Fig. 11. Characterization of the variability of thermal conductivities in presence of an opening.
the case of polystyrene samples, the coefficients are calibrated as: a1 = 19.40; b1 = 3.44; a2 = 73.88; b2 = 6.45 for the groove model (Eq. (6.1)). While in the case of openings (Eq. (6.2)), the thickness of the defect (td) is always equal to the thickness of the insulation (t). Therefore, the impact on the thermal performance would be varying according to the width size (wd) and the coefficient of the opening model (Eq. (6.2)) is found to be a = 2. Fig. 14a and b present the effective thermal conductivity of a 5 cm insulation considering different sizes of grooves and openings, respectively. Fig. 14a presents the impact of the aspect ratio and size of the defect according to the results obtained from the model (Eqs. (5) and (6.1). Fig. 14b shows the impact of different degrees of opening on the thermal conductivity of the insulation given by the proposed model (Eq. (5) and (6.2)).
These results highlight the impact of small defects on the thermal performance of the insulation material. For instance, considering the groove-shaped defect specimens (Fig. 14a) shows that for a deep groove of td = 4 cm over a total thickness of t = 5 cm, the thermal performance losses are more than 98%. While, if the groove is wide (wd = 4 cm over the specimen width w = 30 cm), the thermal conductivity losses are 43%. Regarding openings (Fig. 14b), the results are much worrying. Because of the gap between insulation panels, the insulation is expected to lose more than 27% of its thermal performance for only 1 cm width versus 5.5% when considering the same defect width for a groove of (wd = 1 cm/td = 2 cm). In the case of irregular defects, the crushes and sheath passage defects show that they both affect the geometry of the surrounding area (and not only the defect area as for the first two defect cases; i.e. groove and opening). In this specific case, the analytical model takes the form:
k ¼ k1 ðtÞ þ ðk0 k1 ðtÞÞ exp ½f ðt; td Þ
ð7Þ
with
(
f ðt; t d Þ ¼ e ða1 t b1 Þ ttd 2 k1 ðt; t w Þ ¼ a2 t þ b2 ttw c2 ttw þ d2
ð1Þ ð2Þ
ð8Þ
where k⁄ is the effective thermal conductivity of the material in presence of defect, k0 is the thermal conductivity of the defectfree material (healthy material), k1 the maximum thermal conductivity of the material depending on the insulation thickness t and the importance of defect tw that is represented here by the wooden board thickness or the sheath diameter, td is the crushed thickness of the material at the defect location. The experimental data allows
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Probabilies (%)
a
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.035
MW 5cm - Crush on a wooden board (2cm)
Median Ranks Lognormal Distribuon Normal Distribuon
0.04
0.045
0.05
0.055
0.06
Experimental conducvies (W/mK)
b
c
Fig. 12. Characterization of the variability of thermal conductivities with crushed samples on different wooden board sizes. (a) MW 5 cm – Crush on a wooden board of 2 cm. (b) MW 8 cm – Crush on a wooden board of 2 cm. (c) MW 8 cm – Crush on a wooden board of 4 cm.
1 0.9
Table 2 Summary of the adopted distribution laws for each studied specimens.
MW-Sh
Probabilies (%)
0.8 0.7 0.6
Type of defect
Specimens
Distribution law of k⁄
Mean (W/m K)
Coefficient of variation (%)
EPS-G t = 0.04 m
wd = 0.03 m td = 0.01 m wd = 0.03 m td = 0.02 m
Lognormal
0.0557
0.007
Lognormal
0.0547
0.110
wd = 0.03 m td = 0.02 m wd = 0.05 m td = 0.02 m
Lognormal
0.0500
0.071
Lognormal
0.0480
0.104
MW-O t = 0.08 m
wd = 0.005 m
Lognormal
0.0393
0.0014
MW-C t = 0.05 m
tw = 0.02 m
Lognormal
0.0445
0.091
MW-C t = 0.08 m
tw = 0.02 m tw = 0.04 m
Lognormal Lognormal
0.0447 0.0469
0.172 0.144
MW-Sh t = 0.05 m
td = / = 0.02 m
Lognormal
0.0445
0.067
0.5 0.4 0.3
Median Ranks
0.2
Lognormal Distribuon
0.1 0 0.035
EPS-G t = 0.05 m
Normal Distribuon
0.04
0.045
0.05
0.055
0.06
Experimental Conducvies (W/mK) Fig. 13. Characterization of the variability of thermal conductivities in presence of sheaths (/ = 2 cm).
for the calibration of the model coefficients leading to results in Table 3. Fig. 15a and b present the effective thermal conductivity given by the model for different crushes and sheath diameters, respectively. For the same size of defect, the thermal conductivity obtained with sheath passages is found to be higher than for simple crush, which is explained by the extra heat flow lost through the hole present in MW-Sh insulations. For instance, when a 3 cm diameter sheath crosses a MW specimen of 10 cm, the thermal performance loss reaches 66% versus 57% for a simple crush. In conclusion, this section provides analytical models for estimating the effective thermal conductivity to use in simulations, in order to reach more realistic predictions of energy needs of dwellings. The practical use of this model is shown through the wall application in the following section, where the extra energy consumption is computed.
7. Impact of workmanship errors on the performance of insulated external walls A simple case study is proposed to evaluate the impact of defects on the expected global energy consumption. Consider the external wall presented in Fig. 16, composed of hollow blocks, mineral wool insulation of 8 cm, and inner and external plaster layers. The nominal thermal conductivity given by the manufacturer is 0.035 W/m K. By using the proposed models in Eqs. (5) and (7), the effective thermal conductivity of the material in presence of defects can be determined. All the defects are assumed to be on the widthwise of the insulation panel of 0.3 m width 1 m length. Fig. 17 presents the impact of the size defect on the thermal
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Fig. 14. Thermal conductivity as function of groove type (a) and opening size (b).
Table 3 Coefficients⁄ of Eqs. (8.1) and (8.2).
⁄
Defect type
f (t, td) (Eq. (8.1))
k1 (t, tw) (Eq. (8.2))
e
a1
b1
a2
b2
c2
d2
MW-C Sheath (/ = tw = 2 cm)
1 0.1
56.7 0
69 81
0.316 0
0.0065 0
0.0285 0
0.03 0.06
Fig. 16. External structure of an insulated wall.
Coefficients calibrated thanks to experimental results.
conductivity of the insulation while Fig. 18 presents the evolution of the thermal resistance. These results are significantly worrying and as expected, the insulation resistance is found to be reduced whatever the defect. For instance, for a defect of 2 cm (representing 0.5% of the insulation volume), MW-Sh is considered to be the most compromising defect. In fact, the thermal performance losses are about 40% versus 36% for an opening, 28% for a groove and 12% for a crush. Larger defects make the opening more compromising with more than 60% of performance lost for a defect of 4 cm (defects representing 20% of the insulation volume), versus 44% in the case of a groove, 40% in presence of a sheath and about 20% for a crush. As expected, this loss in thermal resistance of the material brings extra energy consumptions. Fig. 19 presents the evolution of the energy consumption to expect, over 15 years, when the insulation layer is altered by one of the defect types of this study. The total energy consumption considers both Heating (EC,H) and Cooling (EC,C) energy needs. These energy consumptions are calculated each year by [31,38]:
½kW h=m2 =year
ð9Þ
a
EC;C ¼
b
0.075 0.065 0.055 0.045 0.035 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
Material Crush (t-tw) (m)
86; 400 CDD R COP
½kW h=m2 =year
ð10Þ
where gs is the efficiency of the heating system, COP the coefficient of performance of the cooling system, HDD and CDD are respectively
Thermal conducvies (W/mK)
86; 400 HDD R gs
Thermal Conducvity (W/mK)
EC;H ¼
Fig. 17. Evolution of the thermal conductivity according to the defect type and size.
0.06 0.055 0.05 0.045 0.04 0.035 0
0.01
0.02
0.03
Sheath diameter ( m)
Fig. 15. Thermal conductivity as function of the defect size: (a) Crush and (b) Sheath diameter.
0.04
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A. Aïssani et al. / Applied Energy 165 (2016) 272–284 Table 4 Deterministic parameters used for calculations.
Fig. 18. Deterioration of the thermal resistance of the insulation according to defect type and size.
Fig. 19. Evolution of the expected energy consumption according to the thermal performance of the wall.
the heating and cooling degree-days2 [K], describing the local climate. Table 4 gathers the parameters used for this consumption and their values are drawn from the French market. As can be seen from Eqs. (9) and (10), three main parameters affect the energy consumption: 1. The thermal resistance of the wall (R), which is affected by the deterioration of the materials over time because of aging and moisture retaining, in addition to the impact of defects. 2. The heating degree days (HDD) and the cooling degree days (CDD) which depend on: a. Climate, describing the external environment of the wall. Over years, important climate changes are expected, resulting in decreasing heating needs and increasing cooling needs. b. Occupant comfort, governing the inside environment of the wall. This parameter fluctuates from an occupant to another. The total quantity of the annual heating and cooling degreedays is calculated by [31,39]
HDD ¼
HS X þ ðT base T 0 ðiÞÞ
ð11Þ
i¼1
CDD ¼
CD X þ ðT 0 ðiÞ T base Þ
ð12Þ
i¼1
2 The degree-days are generally used to simplify calculations of the annual energy consumption and it is commonly adopted by the building industry to relate trends of building energy consumption to local climate conditions. More details can be found in the works of Kaynakli [40], Aïssani et al. [31].
Parameters
Values
Materials
Thickness
Thermal conductivity
Hollow blocks Internal plaster External plaster Mineral wool
0.200 m 0.014 m 0.015 m 0.080 m
1.60 W/m K 0.32 W/m K 0.25 W/m K 0.35 W/m2 K
Length Width
1m 0.3 m
Fuel Heating efficiency
Electricity 0.6
Coef. of performance of the cooling climate
2.5 Region of ClermontFerrand
Life time (LT)
15 years
where Tbase is the base or reference temperature of the heated or cooled space (e.g. 18 °C in France, 18.5 °C in UK and 18.3 °C in USA), T0 is the average outdoor temperature of the ith day, HS is the average number of heating days and DS is the average number of cooling days. In this study, the climate corresponding to a cold region in France was considered. Over the years, the changes of the climate are still difficult to predict as they depend on many uncontrollable parameters [31]. In this study, it is assumed that the climate follows the worst scenario3 of global warming resulting in decreasing heating needs and increasing cooling needs. In Fig. 19, different predictions of the energy consumptions are presented in the case of walls considering non-defective or defective insulations. We can see that in all cases, the shape of the predictions is very similar. In early years, the total energy consumption is expected to increase due to the deterioration of the thermal resistance of the insulation and to cold winters (High HDD)/warm summers (low CDD). However, with time, the temperatures are expected to increase in winters, resulting in lower HDD/ higher CDD, which explains the observed stabilization of the consumptions. The lowest curve represents the evolution of the energy consumption expected in the case of a wall containing a healthy insulation material. In this case, the insulation performs as expected, and the energy consumption is only affected by variation in climate and the aging of the material. Other predictions presented in Fig. 19 shows the evolution of the energy consumption when considering this time, the insulations altered by the defects studied previously. From a global point of view, it can be clearly notice that the presence of any kind of defect increases the initial energy needs and the gap between the curves is accordingly increased due to the considered type of defect. Under perfect considerations, the energy consumption would be of 21 kW h/m2 annually, while the presence of a groove can lead to more than 3.5 kW h/m2 extra energy consumption per year, and the presence of the worst defect (opening) may lead to more than 15 kW h/m2 annually. Over time, when looking at the predictions associated to the healthy material (lowest curve), the deterioration of the insulation itself results in almost 9.5% in extra energy needs in less than 15 years. However, in presence of defects, it can be seen that the extra need in energy can reach 12% for a groove width defect of 4 cm, versus 19% for a groove depth of the same size; whereas a crushed material needs 23.8% in energy versus 47.6% for a material 3 According to the predictions of the intergovernmental Panel on Climate Change (IPCC).
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crossed by a sheath. Besides, the opening represents the worst defect as the presence of such defect makes the energy consumption 72% higher than the expected one for a defect-free insulation. Remark In case of warm climates, the evolution of the energy consumptions would be obviously lower, as the indoor environment would need less heating and cooling to ensure the comfort temperature. However, the presence of defect would also lead to high heat losses leading to higher energy demand and over time the energy consumption is also expected to increase due to the material deterioration and global warming. 8. Conclusion This paper investigates the impact of workmanship errors on the thermal performance of insulation and proposes analytical models to evaluate the effective thermal conductivity to use in simulation, according to the type and size of defect. Four common defects were studied. An experimental study permitted to evaluate the impact of the defects on the global thermal resistance. Each defect is then modeled using the finite element method. The inverse method allowed us to calibrate the finite element models using experimental results, to evaluate the global effective thermal conductivity representing the effective performance of the defective insulation. To control the energy consumption of buildings, it is important to consider all sources of uncertainty that may affect the predictions, since the early stages of design. For this purpose, this paper proposes two analytical models that can be used to quantify the effective thermal conductivity, in the case of regular defects such as grooves or openings on one hand, and in the case of irregular defects such as crushes and sheath passages on flexible materials, on the other hand. The impact of the defect type is compared and shows that the thermal performance of the insulation is more sensitive to irregular defects than to regular ones. A parametric study allowed us to highlight that openings are the worst defects that can affect the insulation. This is an important issue, as openings often appear over time especially with flexible materials. However, in the case of small sized defects, irregular defects are found to be more affecting the insulation resistance in contrast to grooves and small openings. The studied defects are more often found in walls insulations. A practical application allowed us to quantify the deviation of the energy consumptions, when considering the different defects, compared to the expected energy consumption of a healthy material. The results show that the energy consumption is highly influenced by the quality of the installation, regardless to indoor and outdoor environmental conditions. As a matter of fact, it was also noticed that the presence of openings resulted in the highest energy consumptions. The uncertainty analysis showed that the scatter of experimental results increases with larger amount of defect. The highest dispersions are observed for irregular defects that commonly affect flexible materials. Besides, the lognormal distribution appeared to fit well the experimental distribution of results. For the decision making purposes, the obtained statistical results provide useful information to use in the simulation process and for reliability analyses. 8.1. Practical recommendations This work showed that the thermal resistance of insulations was significantly affected by all kinds of defect. However, some defects appeared to be more harmful than others.
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As a matter of fact, all kinds of defects creating holes or openings must be avoided in practice, as they induce very important heat losses resulting in non-negligible extra energy consumptions. This paper showed that insulation panels were found to lose more than 36% of their performance when openings represent only 0.5% the total insulation volume. Besides, the losses increase exponentially when the defect volume increases. Openings are unacceptable defects and must be avoided at any time. In case of flexible materials, it is common to observe crushes in insulation panels. If the panel is crushed at 0.5% of its total volume, the insulation losses almost 12% of its initial performance. That means that, if we want to maintain the initial consumption predictions, the insulation should be oversized of at least 12%. In case of synthetic materials, construction site workers are used to perform grooves to make electrical wires and sheath standing on the walls. It is found that wide grooves are less harmful than deep grooves, as the insulation thicknesses are less affected with wide grooves. For a groove representing 0.5% of the total insulation volume, it is recommended to oversize the insulation of 40% if the groove is in depth versus 28% if the groove is in width. In addition, it was found that the performance of synthetic materials is more controlled. Indeed, even if the initial performance of polystyrene is expected to be lower than the one of fibrous materials, it seems that the performance scatter of such material is lower than fibrous materials. The proposed models may be helpful to set the maximum bearable workmanship defects. However, it could be interesting to conduct in situ measurements and consider expert judgments to set average defaults sizes commonly observed on construction sites, to be used in simulation. Hence, according to the type of the material, the type and size of defect, designers would be able to anticipate an average over sizing of the insulation. References [1] Al-Sanea S, Zedan M, Al-Hussain S. Effect of masonry material and surface absorptivity on critical thermal mass in insulated building walls. Appl Energy 2013;102:1063–70. [2] Martin K, Escudero C, Erkoreka A, Flores I, Sala J. Equivalent wall method for dynamic characterisation of thermal bridges. Energy Build 2012;55:704–14. [3] Bond D, Clark W, Kimber M. Configuring wall layers for improved insulation performance. Appl Energy 2013;112:235–45. [4] Abdou A, Budaiwi I. The variation of thermal conductivity of fibrous insulation materials under different levels of moisture content. Constr Build Mater 2013;43:533–44. [5] Wei S, Fu-chi W, Qun-Bo F, Zhuang M. Effects of defects on the effective thermal conductivity of thermal barrier coatings. Appl Math Model 2012;36:1995–2002. [6] Lu Y, Huang Z, Zhang T. Method and case study of quantitative uncertainty analysis in building energy consumption inventories. Energy Build 2014;57:2159–68. [7] Dominguez-Munoz F, Anderson B, Cejudo-Lopez J, Carrillo-Andrés A. Uncertainty in thermal conductivity of insulation materials. Energy Build 2010;42:2159–68. [8] Aïssani A, Chateauneuf A, Fontaine J-P, Audebert P. Cost model for optimum thicknesses of insulated walls considering indirect impacts and uncertainties. Energy Build 2014;84:21–32. [9] Spitz C, Mora L, Wurtz E, Jay A. Practical application of uncertainty analysis and sensitivity analysis on an experimental house. Energy Build 2012;55:459–70. [10] Hopfe C, Hensen J-L. Uncertainty analysis in building performance simulation for design support. Energy Build 2011;43:1010–8. [11] Asdrubali F, Baldinelli G, Bianchi F. A quantitative methodology to evaluate thermal bridges in buildings. Appl Energy 2012;97:365–73. [12] El Diasty R. Effect of thermal anomalies and dimensional instability on building envelopes performance. Constr Build Mater 1988;2. [13] C.E.c.f. standardization, ISO 10211:2007: thermal bridges in building construction – international standards, BSI British standards; 2007. [14] Sierra F, Bai J, Maksoud T. Impact of the simplification of the methodology used to assess the thermal bridge of the head of an opening. Energy Build 2015;87:342–7. [15] Ibrahim M, Biwole PH, Wurtz E, Achard P. Limiting windows offset thermal bridge losses using a new insulating coating. Appl Energy 2014;123:220–31. [16] Zalewski L, Lassue S, Rousse D, Boukhalfa K. Experimental and numerical characterization of thermal bridges in prefabricated building walls. Energy Convers Manage 2010;51:2869–77.
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[17] Capozzoli A, Gorrino A, Corrado V. A building thermal bridges sensitivity analysis. Appl Energy 2013;107:229–43. [18] Al-Sanea SA, Zedan M. Effect of thermal bridges on transmission loads and thermal resistance of building walls under dynamic conditions. Appl Energy 2012;98:584–93. [19] Taylor T, Counsell J, Gill S. Combining thermography and computer simulation to identify and assess insulation defects in the construction of building façades. Energy Build 2014;76:130–42. [20] Taylor T, Counsell J, Gill S. Energy efficiency is more than skin deep: improving construction quality control in new-build housing using infrared thermography. Energy Build 2013;66:222–31. [21] Hopper J, Litterwood J, Taylor T, Counsell J, Thomas A, Karani G, et al. Assessing retrofitted external wall insulation using infrared thermography. Struct Survey 2013;30:245–66. [22] Grinzato E, Vavilov V, Kauppinen T. Quantitative infrared thermography technique in building finish evaluation. J Non-destruct Eval 2000;12:9–11. [23] Wrobel A, Kisilewicz T. Detection of thermal bridges-aims, possibilities and conditions. chez Qirt, Kracow, Poland, 2–5 july, 2008. [24] Benko I. Quantitative analysis of thermal bridges of structures through infrared thermographs, chez Qirt, Dubrovnik, Croatia, 24–27 September, 2002. [25] Fox M, Coley D, Goodhew S, De Wilde P. Thermography methodologies for detecting energy related building defects. Renew Sustain Energy Rev 2014;40:296–310. [26] Theodosiou T, Papadopoulos A. The impact of thermal bridges on the energy demand of buildings with double brick wall constructions. Energy Build 2008;11:2083–9. [27] Bras A, Gonçalves F, Faustino P. Cork-based mortars for thermal bridges correction in a dwelling: thermal performance and cost evaluation. Energy Build 2014;72:296–308.
[28] Evola G, Margani G, Marletta L. Energy and cost evaluation of thermal bridge correction in mediterranean climate. Energy Build 2008;40:2083–9. [29] De Wilde P, Tian W, Augenbroe G. Longitudinal prediction of the operational energy use of buildings. Build Environ 2011;46:1670–80. [30] de Wit S, Augenbroe G. Uncertainty in developing supervisory demand-side controls in buildings: a framework and guidance. Automat Constr 2013;35: 28–43. [31] Aïssani A, Chateauneuf A, Fontaine J-P. Reliability based design optimization of insulation systems considering climate change and workmanship uncertainties. In: Proceedings of the 12th international conference on applications of statistics and probability in civil engineering; July 2015. [32] Heo Y, Choudhary R, Augenbroe GA. Calibration of building energy models for retrofit analysis under uncertainty. Energy Build 2012;47:550–60. [33] Thermal insulation-determination of steady-state thermal resistance and related properties-guarded hot plate apparatus, ISO 8302, 1991-08-01. [34] VISULOG, monitoring, control and data acquisition software. F.18(6,18,0,0) Copyright 2003–2005 AOIP. [35] Fokaides P, Kalogirou S. Application of infrared thermography for the determination of the overall heat transfer coefficient (U-value) in building envelopes. Appl Energy 2011;88:4358–65. [36] FLIR Silver Model, User Manual – 2003. FLIR company, Extech instrumentsInfraRed Thermometer. [37] I.a.A.E. ANSYS, ANSYS Release 14.0 UP20111024. Copyright 2011 SAS IP, Inc. [38] Comakli K, Yüksel B. Optimum insulation thickness of external walls for energy savings. Appl Therm Eng 2003;23:473–9. [39] Guan L. Preparation of future weather data to study the impact of climate change on building. Build Environ 2009;42:793–1172. [40] Kaynakli O. A review of the economical and optimum thermal insulation thickness for building applications. Renew Sustain Energy Rev 2012;16:415–25.
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Quantification of workmanship insulation defects and their impact on the thermal performance of building facades A. Aïssani ⇑, A. Chateauneuf, J.-P. Fontaine, Ph. Audebert Blaise Pascal University, University of Clermont Ferrand, Pascal Institute, Clermont Ferrand, France CNRS, UMR 6602, Institut Pascal, F-63171 Aubière, France
h i g h l i g h t s Effective performance of insulations altered by practical defects is investigated. Analytical models evaluate the effective thermal conductivity to use in simulation. The energetic impact of common workmanship defects is studied. The presence of openings in facade walls are found to be more harmful.
a r t i c l e
i n f o
Article history: Received 14 September 2015 Received in revised form 27 November 2015 Accepted 13 December 2015 Available online 4 January 2016 Keywords: Insulation Effective thermal conductivity Workmanship defects Uncertainties
a b s t r a c t Nowadays, many performing insulation materials are available on the market. However, their expected thermal performance can be affected by many sources of uncertainty due to random errors that can occur during the manufacturing and the measurement processes. In addition, the thermal performance is strongly affected by another source of uncertainty related to the insulation laying process. As a matter of fact, defects in insulation panels are introduced either for practical reasons or due to a lack of rigor of workers. These errors are still not yet properly considered for simulation, although they result in significant heat losses. This work aims at investigating the impact of four common workmanship errors on the thermal performance of insulation panels. A coupling between experimental measurements and finite element modeling allows us to evaluate the effective thermal conductivity of insulations in presence of defects. The uncertainty analysis allows us to quantify the dispersion of insulation conductivity according to different types and sizes of defects, showing that flexible materials seem to be more affected by the defects. For simulation purpose, analytical models are proposed to assess the effective thermal conductivity in terms of size and type of defects. The numerical application to insulated wall shows the impact of each defect on the energy consumption, where deep grooves and openings are found to be strongly affecting the insulation performance. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The demand for energy is increasing worldwide because of increasing population and improving standards of living [1]. In the building sector, the greatest potential for energy savings is based on the reduction of heating and cooling [2], which helps to reduce building operating costs as well as emissions and other related impacts [3]. Therefore, more attention to the envelope insulation design is required, as it remains the first protection of ⇑ Corresponding author at: Blaise Pascal University, University of Clermont Ferrand, Pascal Institute, Clermont Ferrand, France. E-mail address: [email protected] (A. Aïssani). http://dx.doi.org/10.1016/j.apenergy.2015.12.040 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
the inner space from the outdoor climatic conditions. Physically, the effectiveness of the insulation depends on its thermal conductivity (k-value) and its ability to maintain its thermal characteristics over a large period of time [4]. In practice, to evaluate the coefficient of transmittance (U) of insulation, the k-values provided by manufacturers are used. However, This U-value is altered by other parameters, such as the presence of defects in the insulation materials themselves, as well as defects that occur during the installation procedures [5]. In the literature, several studies highlighted the uncertainty on insulation properties, due to the intrinsic variation of material properties, mankind errors, unavoidable measurement errors, random errors and non-representativeness of sample data, etc. [6,7].
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For instance, Dominguez-Munoz et al. [7] estimated the uncertainty that can be expected in the thermal conductivity of insulation materials for known and unknown material density. They finally provided a generic estimation of the uncertainties affecting the thermal conductivity of common insulation materials. For energy simulations, available models consider the deterministic thermal properties of materials, drawn from inspections and manufacturing measurements [6]. Moreover, several studies such as those of Aïssani et al. [8], Spitz et al. [9] and Hopfe and Hensen [10] considered these physical uncertainties in simulations to support decision making in Building Performance Simulation (BPS). However, in addition to the above mentioned material uncertainties, it is extremely important to consider the uncertainties associated to the weakest components of the envelope [11,12]. In insulated buildings, three kinds of defects can be observed 1. Design defects: these defects represent the Thermal Bridges (TBs) in building blocks. They generally occur at any junction between building components or where the structure composition changes [13–17]. 2. Implementation or workmanship defects: these defects are induced for practical reasons during retrofitting. They are most of the time due to the lack of rigor of workers, but also due to insufficient site controls. 3. Lifetime defects: these defects are due to aging, material settlement or moisture damage in building insulations. The presence of Design Defects (i.e. TBs), and implementation defects are the principal sources of heat losses in insulated buildings. Despite the fact that they are common rather than exceptional, their effects have been largely ignored in simulations although their presence reduces significantly the global thermal resistance of the envelope. As a consequence, the transmission loads increase and accordingly these defects jeopardize the beneficial use of thermal insulation [18], especially when high performance is required, such as in the case of low consumption buildings. In the literature, several studies have provided evidence that thermal performance of new dwellings can be significantly below the design predictions and investigated the scope for conducting thermographic inspections during the construction process to support the management of construction quality on sites [19–25]. Martin et al. [2] recognized that their impact is not yet properly calculated in building energy demand and as far as energy saving is concerned it can only be asserted that the proportion of the defect impact increases when the insulation quality of the envelope becomes higher. Up to now, most of scientists focus on TBs and lifetime degradations and most of their studies deal with thermal losses at junctions between two separately insulated elements, losses at openings or between vertical and horizontal elements [26,27] on one hand, while others deal with the lifetime defects due to cooling the interior surfaces and the resulting higher condensation which concerns molds and fungi growth [28,27], on the other hand. Regarding workmanship defects, it is still difficult to evaluate their exact impact on energy predictions as workmanship varies from a construction site to another. In practice, unlike other industries, the building industry cannot specify performance with zero allowances due to the variations of these workmanships [12]. In practice, many technical reports and scientific papers discuss workmanship anomalies [12] and most of them are aware that the observed deviation in performance of buildings is due to bad workmanship [12,29] in addition to other sources of uncertainty such as climate and efficiency of energy systems. However, with the emergence of the new energy requirements, it becomes important to consider these sources of uncertainty.
273
In the literature, many works underlines the importance of considering these kinds of uncertainties for better performance predictions [30–32]. However, it is still difficult to consider these uncertainties because of lack of data [31], as almost no study properly addresses the quantitative impact of workmanship to consider in performance predictions. Workmanship anomalies are most of the time part of the building facade and can be a big source of heat losses. In this framework, the present study implementation defects that are due to shortcomings during the insulation laying. An experimental study is proposed to investigate usual irregularities induced by retrofitting. To do so, a coupling between the guarded heat plate method and thermography puts in evidence the heat flow losses induced by four types of defects (Fig. 1): Groove defect (EPS-G), which is generally found in homogenous and dense insulation materials. It consists in a regular and localized defect that appears when trying to make sheaths and electric wires passing through materials that cannot be crushed. Opening (MW-O), which is generally found between two insulation panels. This kind of defects is more noteworthy over time, as mineral wool collapses and openings become larger. As a matter of fact, this kind of defect may be considered as a special case of a groove-shaped defect, where the depth of the defect is equal to the total thickness of the insulation material. Crush defect (MW-C), which is the defect that is generally identified with flexible materials such as rock wool or glass wool. In contrast to the previous regular and localized defects, crushes make the geometry of the insulation material varies beyond the crush point. Sheath passage (MW-Sh). In this case, the presence of sheath in the insulation crushes partially the insulation panel, in addition to the presence of a gap around the sheath when it crosses the material. An experimental and numerical coupling has been carried out to develop analytical models representing the effective thermal conductivity of the insulation in presence of implementation defects. Finally, an application on an insulated external wall, including defects, is proposed to evaluate the energetic impact of these defects on the performance.
2. Experimental study To quantify the impact of implementation defects, an experimental work has been undertaken under laboratory conditions at the University Blaise Pascal, in France. The loss in thermal conductivity is assessed for each defect type and the uncertainty on the expected conductivity is then described by probabilistic distributions. The experimental set up (Fig. 2) seeks at determining the thermal resistance of insulation specimens by the mean of the Guarded Hot Plate, as presented in the standards ISO 8302 [33]. Each sample is standardized to 300 300 mm and at least 50 mm of thickness is adopted. The Guarded Hot Plate method allows the determination of the thermal resistance of homogenous and inhomogeneous specimens of parallelepiped forms. The test samples are sandwiched between hot and cold plates and a constant heat flow is imposed through the samples for a steady state transfer. To do so, it is important to achieve a quasi-stationary heat transfer state during the measurement with at least 2 or 3 h beforehand. Fig. 2 describes the apparatus used in this study. To determine the temperatures of the inner and outer sides of the specimens, temperature sensors are installed. On the inside face of the samples in contact to the
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EPS-G
MW-C
MW-Sh
MW-O
Fig. 1. Presentation of the different studied defects.
Fig. 2. Description of the apparatus used and its equivalent electric block diagram.
heating plate (guarded plate), a modular processing system is associated to k-type thermocouples to record heating temperatures (Tins). These data are then stored using VISULOG [34] traceability software. This tool allows the monitoring and the management of measurement channels of thermocouples. Over the measured
zone (Fig. 3), the thermocouples are distributed in order to have the best evaluation of the temperature field applied to the specimen. Fig. 4 shows the thermocouple locations. To determine the temperatures on the outer surface of the specimens (Tout), infrared thermography is used. The whole heat
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275
Fig. 3. Elements constituting the guarded hot plate apparatus.
heat losses at the specimen borders (Fig. 5). In addition, a thermohygrometer was used to determine the ambient atmospheric conditions i.e. ambient temperature and relative humidity. Fig. 6 gives a picture of the apparatus with and without the surrounding framework. To determine the resistance of the samples (R-value), it is assumed that the heat transfer from the wall to the thermal camera sensor is due to thermal radiation (Rad) and convection (conv) (Fig. 2), defined by [35]:
Q_ Rad ¼ 4 Q_ conv ¼ hIN
e r A T 3W ðT W T OUT Þ A ðT w T INS Þ
ð1Þ ð2Þ
where Q_ Rad and Q_ conv are the radiative and convective thermal heat flows [W] respectively, e is the thermal emissivity (0.94), r is the Stefan–Boltzmann constant, A is the section of the studied area [m2], hIN is the convective heat transfer coefficient [W/m2 K] for indoor environment, TW is the temperature of the wall [K], TOUT is the reflected outer area temperature [K] given by the thermal camera and TINS is the inner area temperature [K]. The thermal resistance R is then obtained from the total heat flow Q_ : Fig. 4. Thermocouples position at the measurement zone.
transfer processes of the thermography analysis, is sketched and detailed in the works of Fokaides and Kalogirou [35] and Asdrubali et al. [11]. For our testing procedure, an IR-camera manufactured by FLIR [36] (silver model with 320 256 pixel resolution) allowed us to record the different infrared radiations that are emitted by the specimens. Each image gives the temperature of each pixel hit by the radiation emitted from the tested object, defining the entire thermal field of the area covered by the camera optic [11]. The reflective properties of the surface itself can be corrected by applying masking or flat black painting or tapes to the surface before testing [36,35]. In this study, to have a good overview of the heat losses, flat black paint was applied on samples. For experimental purposes, a surrounding structure made of mineral wool has been set up to reduce convective and conductive
A Q_ ¼ ðT INS T OUT Þ R
ð3Þ
ðT INS T OUT Þ i R¼h ðhINS ðT W T INS ÞÞ þ 4erT 3W ðT W T OUT Þ
ð4Þ
Fig. 7 depicts the temperatures captured by the IR-Camera at the outer surface of the specimens shown in Fig. 1. Obviously, the highest temperatures are found at the defects locations, which indicate higher heat losses at these points. 3. Experimental and numerical coupling To determine the effective thermal conductivity of the specimens, a coupling between experimental results and numerical modeling is carried out. Fig. 9 depicts the procedure adopted for the assessment of this equivalent thermal conductivity:
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on the guarded hot plate tests, in order to determine the effective thermal conductivity of the for different defect sizes and insulation thicknesses. For each configuration, the number of tested specimens is between four and six. All of them come from the same manufacturer with the same density, thickness and thermal characteristics. The results are given in Table 1. In the case of EPS-G, the results are presented for different groove widths wd and depths td. The results show clearly the impact of the groove dimensions on the thermal conductivity of the insulation component. More specifically, it is observed that:
Fig. 5. Surrounding framework to reduce edge effects.
– First, all the defects are modeled using the Finite Element Method (FEM), using, Ansys software [37]. Although a regular mesh can be adopted for regular defects (grooves and openings), a tetrahedral mesh is considered for complex defects (crushes and Sheath passages) in order to allow for more accurate representation of the defect geometry (Fig. 8). The finite element analysis is applied by using experimental boundary conditions where a steady state transfer is considered. The specimen’s lateral borders are subjected to zero flux boundary conditions. The temperature field measured during testing (TINS) is applied on the inner surface of the finite element model and convective boundary conditions are applied on the outer surface. – To deduce the effective thermal conductivity, noted k⁄, of the insulation in presence of defects, the Finite Element (FE) analysis is performed for different thermal conductivities k and the resulting outer surface temperatures (Tout-FE) are compared to those observed experimentally (Tout-IR). An optimization procedure is then applied to define the effective conductivity k⁄ that better fits the experimental results, by minimizing the squared difference between the experimental and numerical temperature fields (i.e. inverse problem solution).
4. Effective thermal conductivity The effective thermal conductivity k⁄ is defined herein as the conductivity to adopt in case of defective insulation materials. The procedure described in the above section is now applied
1. The thermal conductivity losses increase significantly with the groove volume. Indeed, the average value of the obtained thermal conductivity increases when the groove width or depth increase. 2. The aspect ratio of the defect is also crucial. In fact, the thermal conductivity is more sensitive to deeper grooves td than to wider grooves wd. 3. The conductivity scatter is higher for small groove widths, and for high groove depths. This is emphasized by the fact that the thermal conductivity is more sensitive to the depth of the groove. Indeed, the affected area is larger with wide defects but losses are quasi-homogeneous over the affected area; whereas deep defects affect a smaller area of the material that makes high fluctuations of the thermal resistance between the affected area and the rest of the insulation, resulting in more dispersed results. In the case of MW-O defects, it is seen that the effective thermal conductivity is obviously affected by the gap created between the insulation panels. This gap induces concentration of heat losses at the opening location (Fig. 7-MW-O) resulting in losses in the thermal performance. The results of Table 1 indicate that the average thermal conductivity of the samples is 11% higher than the expected conductivity of the mineral wool without defects, when considering an opening of only 5 mm. However, the variability in experimental results does not exceed 2% due to the fact that the studied openings are very small. Nevertheless, over time the opening tends to be higher because of the aging and the collapse of materials, leading to higher heat losses and thus higher variabilities in the same way as wide grooves. In addition, higher dispersion is expected as mineral wool specimens are naturally flexible that makes each specimen more or less affected by the defect. In the case of MW-Crush defects, it is important to note that results do not consider the thermal resistance of wooden board on which the material is crushed and results present only the effective thermal conductivity of the crushed mineral wool. Obviously,
Fig. 6. Picture of the apparatus used in the laboratory; (a) measure zone without the surrounding framework; (b) measure zone surrounded by the mineral wool framework.
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EPS -G
MW -O
MW -C
MW -Sh
Fig. 7. Finite-Element meshes of the insulation panels with different types of defects.
EPS-G
MW-O
MW-C
MW-Sh
Fig. 8. Outside temperatures (Tout-IR) of the defective specimens by the IR-Camera.
Fig. 9. Effective thermal conductivity assessment procedure.
conductivities are found to be strongly altered by the crush level. As a matter of fact, when the crush is deep, the affected area around the crush point becomes wider, resulting in higher thermal losses. Moreover, the variability in testing results is found to be higher in this kind of defect. Besides, this variability obviously increases when the degree of crush is higher. Finally, the last studied defect is the MW-Sh defects, which could be seen as an extension of the crush defect, as the material is partially crushed. However, the presence of passage hole creates an extra noticeable losses even with the fact that the hole is partially filled by the sheath. The results presented in Table 1 correspond to MW specimens of 5 cm thicknesses and crossed by sheaths of 2 cm diameter. The effective thermal conductivity seems to be affected as much as the case of crushes. From Table 1, it can be seen that the average thermal conductivity obtained for MW-Sh is the same as the one obtained for MW-C in the case of a 2 cm defect. However, the coefficient of variation of the testing results is higher for crushes as the material is geometrically more affected.
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Table 1 Effective conductivity for tested specimens with different types of defects.
5. Probability distribution of the thermal conductivity In order to investigate the experimental distributions of the thermal conductivity, the median rank method is applied, usually recommended for small size of samples. In the case of EPS-Groove defect, Fig. 10a and b compare the experimental conductivity distributions to the normal and lognormal Cumulative Distribution Functions (CDF). The observation of the graphs in Fig. 10 shows that both Normal and Lognormal distributions seem to fit properly the experimental distributions. However, the goodness-of-fit test (Kolmogorov–Smirnov) allows us to consider that the lognormal distribution is more suitable. In all presented case studies of grooves, the hypothesis that the samples are drawn from normal and lognormal distributions is not rejected but the p-value1 returned by the test provides a decision on the best distribution. For all the studied EPS-G defects, the p-values were higher with the lognormal distribution, which allow us to recommend this distribution. Regarding the MW-Opening defect, Fig. 11 compares the CDF obtained with the median rank method to the normal and lognormal CDF. The observed uncertainty on the results is not significant. Although both normal and lognormal distributions seem to match the empirical CDF, the lognormal is preferred as the conductivity is a non-negative property. In contrast to previous regular and localized defects, crushes make the geometry of the material affected beyond the crush point. The final shape of the insulation in presence of crushes differs from a specimen to another due to the flexible and non homogeneous nature of mineral wool. This observation is reflected on the variability of conductivity values as shown in Table 1 and Fig. 12. Fig. 12a and b shows respectively the cases of 5 cm and 8 cm MW specimens crushed with 2 cm thick, and Fig. 12c shows the case of 8 cm MW specimens crushed with 4 cm thick. Experimentally, it has been noticed that, obviously large crushes make the affected area around the crush larger, resulting in an increase in the scatter of results. Both normal and lognormal distributions seem to fit the empirical CDF obtained as shown in Fig. 12a. However, only the lognormal distribution stands out and seems to better fit the experimental results in Fig. 12b and c. In the case of MW-Sheath defect, the impact of the passage hole makes the conductivities higher than a simply crushed insulation material. Indeed, it is clearly noticed that the effective 1 p-value is a value provided by the statistical tests. In general, statistical tests are used to test the validity of a claim (called null hypothesis) that is made on a statistical data set. The p-value can be interpreted in terms of a hypothetical repetition of the study. For instance, suppose the null hypothesis is true and a new data set is obtained independently of the first data set but using the same sampling procedure. The pvalue is the probability to obtain with the new data set the same decision of the test statistic as the original value.
conductivities of a simple crush of a 5 cm MW on a 2 cm wooden board (Fig. 12a) are better than the ones obtained in Fig. 13. Representing the MW (5 cm) crossed by a sheath of 2 cm diameter. Table 2 summarizes the adopted probability distributions and parameters which better describe the variability of the studied samples. In all cases, the hypothesis that the samples are drawn from normal and lognormal distributions was not rejected. From this table, it can be seen that results are accurate as the coefficient of variation is small. The variability associated to conductivity results is due to various measurement errors (random and epistemic) but also to the nature of the used material. As expected, uncertainties are higher using mineral wool specimens. These results give an idea about the scatter of the conductivity according to the defect considered. The advantage of describing variabilities using distribution laws help to support decision making process in building energy simulations. 6. Effective conductivity models for insulation with defects In this section, analytical models are proposed for BPS purposes. These models enable the assessment of the effective thermal conductivity to adopt according to the defect type and size. The first two defect types (i.e. groove and opening) can be considered as similar kinds of defects, as the groove and the opening are regular and localized defects. For these two types of defects, an analytical model is proposed for the assessment of the effective thermal conductivity to describe the insulation panel behavior in presence of such type of defects:
k ¼ k0 exp ½f ðt d ; wd Þ
ð5Þ
8 h i < f ðt d ; wd Þ ¼ a1 td 3 wd 2 þ b td 2 wd ða2 wd þ b Þ Groov e ð1Þ 1 t 2 G t w w w : f ðt ; w Þ ¼ a wd with t ¼ t Opening d d O d t d
ð2Þ ð6Þ
where k⁄ is the effective thermal conductivity of the material in presence of defects [W/m K], k0 is the thermal conductivity of the defect-free material (healthy material) [W/m K], fG and fO are the functions to use at the exponential power in Eq. (5) in case of groove-shaped and opening defects respectively, td is the defect depth [m], wd is the defect width [m], t is the thickness of the insulation panel [m] and w is the width of the insulation panel [m]. Both equations (Eqs. (6.1) and (6.2)) involve the geometrical dimensions of the defect and the size of the panel. In the case of grooves (Eq. (6.1)), we notice that whether the groove is deep or wide, the impact on the thermal performance is not the same. The impact in this case is more affected by the ratios (td/t) than (wd/w). The coefficients were then calibrated by using the experimental results, and may change from a material to another. In
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Fig. 10. Scatter of thermal conductivity in terms of groove width (a) and depth (b).
1
MW - O width = 5mm
0.9
Probabilies (%)
0.8 0.7 0.6 0.5 0.4 0.3
Median ranks
0.2
Lognormal Distribuon
0.1 0 0.035
Normal Distribuon 0.04
0.045
Experimental Conducvies (W/mK) Fig. 11. Characterization of the variability of thermal conductivities in presence of an opening.
the case of polystyrene samples, the coefficients are calibrated as: a1 = 19.40; b1 = 3.44; a2 = 73.88; b2 = 6.45 for the groove model (Eq. (6.1)). While in the case of openings (Eq. (6.2)), the thickness of the defect (td) is always equal to the thickness of the insulation (t). Therefore, the impact on the thermal performance would be varying according to the width size (wd) and the coefficient of the opening model (Eq. (6.2)) is found to be a = 2. Fig. 14a and b present the effective thermal conductivity of a 5 cm insulation considering different sizes of grooves and openings, respectively. Fig. 14a presents the impact of the aspect ratio and size of the defect according to the results obtained from the model (Eqs. (5) and (6.1). Fig. 14b shows the impact of different degrees of opening on the thermal conductivity of the insulation given by the proposed model (Eq. (5) and (6.2)).
These results highlight the impact of small defects on the thermal performance of the insulation material. For instance, considering the groove-shaped defect specimens (Fig. 14a) shows that for a deep groove of td = 4 cm over a total thickness of t = 5 cm, the thermal performance losses are more than 98%. While, if the groove is wide (wd = 4 cm over the specimen width w = 30 cm), the thermal conductivity losses are 43%. Regarding openings (Fig. 14b), the results are much worrying. Because of the gap between insulation panels, the insulation is expected to lose more than 27% of its thermal performance for only 1 cm width versus 5.5% when considering the same defect width for a groove of (wd = 1 cm/td = 2 cm). In the case of irregular defects, the crushes and sheath passage defects show that they both affect the geometry of the surrounding area (and not only the defect area as for the first two defect cases; i.e. groove and opening). In this specific case, the analytical model takes the form:
k ¼ k1 ðtÞ þ ðk0 k1 ðtÞÞ exp ½f ðt; td Þ
ð7Þ
with
(
f ðt; t d Þ ¼ e ða1 t b1 Þ ttd 2 k1 ðt; t w Þ ¼ a2 t þ b2 ttw c2 ttw þ d2
ð1Þ ð2Þ
ð8Þ
where k⁄ is the effective thermal conductivity of the material in presence of defect, k0 is the thermal conductivity of the defectfree material (healthy material), k1 the maximum thermal conductivity of the material depending on the insulation thickness t and the importance of defect tw that is represented here by the wooden board thickness or the sheath diameter, td is the crushed thickness of the material at the defect location. The experimental data allows
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Probabilies (%)
a
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.035
MW 5cm - Crush on a wooden board (2cm)
Median Ranks Lognormal Distribuon Normal Distribuon
0.04
0.045
0.05
0.055
0.06
Experimental conducvies (W/mK)
b
c
Fig. 12. Characterization of the variability of thermal conductivities with crushed samples on different wooden board sizes. (a) MW 5 cm – Crush on a wooden board of 2 cm. (b) MW 8 cm – Crush on a wooden board of 2 cm. (c) MW 8 cm – Crush on a wooden board of 4 cm.
1 0.9
Table 2 Summary of the adopted distribution laws for each studied specimens.
MW-Sh
Probabilies (%)
0.8 0.7 0.6
Type of defect
Specimens
Distribution law of k⁄
Mean (W/m K)
Coefficient of variation (%)
EPS-G t = 0.04 m
wd = 0.03 m td = 0.01 m wd = 0.03 m td = 0.02 m
Lognormal
0.0557
0.007
Lognormal
0.0547
0.110
wd = 0.03 m td = 0.02 m wd = 0.05 m td = 0.02 m
Lognormal
0.0500
0.071
Lognormal
0.0480
0.104
MW-O t = 0.08 m
wd = 0.005 m
Lognormal
0.0393
0.0014
MW-C t = 0.05 m
tw = 0.02 m
Lognormal
0.0445
0.091
MW-C t = 0.08 m
tw = 0.02 m tw = 0.04 m
Lognormal Lognormal
0.0447 0.0469
0.172 0.144
MW-Sh t = 0.05 m
td = / = 0.02 m
Lognormal
0.0445
0.067
0.5 0.4 0.3
Median Ranks
0.2
Lognormal Distribuon
0.1 0 0.035
EPS-G t = 0.05 m
Normal Distribuon
0.04
0.045
0.05
0.055
0.06
Experimental Conducvies (W/mK) Fig. 13. Characterization of the variability of thermal conductivities in presence of sheaths (/ = 2 cm).
for the calibration of the model coefficients leading to results in Table 3. Fig. 15a and b present the effective thermal conductivity given by the model for different crushes and sheath diameters, respectively. For the same size of defect, the thermal conductivity obtained with sheath passages is found to be higher than for simple crush, which is explained by the extra heat flow lost through the hole present in MW-Sh insulations. For instance, when a 3 cm diameter sheath crosses a MW specimen of 10 cm, the thermal performance loss reaches 66% versus 57% for a simple crush. In conclusion, this section provides analytical models for estimating the effective thermal conductivity to use in simulations, in order to reach more realistic predictions of energy needs of dwellings. The practical use of this model is shown through the wall application in the following section, where the extra energy consumption is computed.
7. Impact of workmanship errors on the performance of insulated external walls A simple case study is proposed to evaluate the impact of defects on the expected global energy consumption. Consider the external wall presented in Fig. 16, composed of hollow blocks, mineral wool insulation of 8 cm, and inner and external plaster layers. The nominal thermal conductivity given by the manufacturer is 0.035 W/m K. By using the proposed models in Eqs. (5) and (7), the effective thermal conductivity of the material in presence of defects can be determined. All the defects are assumed to be on the widthwise of the insulation panel of 0.3 m width 1 m length. Fig. 17 presents the impact of the size defect on the thermal
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Fig. 14. Thermal conductivity as function of groove type (a) and opening size (b).
Table 3 Coefficients⁄ of Eqs. (8.1) and (8.2).
⁄
Defect type
f (t, td) (Eq. (8.1))
k1 (t, tw) (Eq. (8.2))
e
a1
b1
a2
b2
c2
d2
MW-C Sheath (/ = tw = 2 cm)
1 0.1
56.7 0
69 81
0.316 0
0.0065 0
0.0285 0
0.03 0.06
Fig. 16. External structure of an insulated wall.
Coefficients calibrated thanks to experimental results.
conductivity of the insulation while Fig. 18 presents the evolution of the thermal resistance. These results are significantly worrying and as expected, the insulation resistance is found to be reduced whatever the defect. For instance, for a defect of 2 cm (representing 0.5% of the insulation volume), MW-Sh is considered to be the most compromising defect. In fact, the thermal performance losses are about 40% versus 36% for an opening, 28% for a groove and 12% for a crush. Larger defects make the opening more compromising with more than 60% of performance lost for a defect of 4 cm (defects representing 20% of the insulation volume), versus 44% in the case of a groove, 40% in presence of a sheath and about 20% for a crush. As expected, this loss in thermal resistance of the material brings extra energy consumptions. Fig. 19 presents the evolution of the energy consumption to expect, over 15 years, when the insulation layer is altered by one of the defect types of this study. The total energy consumption considers both Heating (EC,H) and Cooling (EC,C) energy needs. These energy consumptions are calculated each year by [31,38]:
½kW h=m2 =year
ð9Þ
a
EC;C ¼
b
0.075 0.065 0.055 0.045 0.035 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
Material Crush (t-tw) (m)
86; 400 CDD R COP
½kW h=m2 =year
ð10Þ
where gs is the efficiency of the heating system, COP the coefficient of performance of the cooling system, HDD and CDD are respectively
Thermal conducvies (W/mK)
86; 400 HDD R gs
Thermal Conducvity (W/mK)
EC;H ¼
Fig. 17. Evolution of the thermal conductivity according to the defect type and size.
0.06 0.055 0.05 0.045 0.04 0.035 0
0.01
0.02
0.03
Sheath diameter ( m)
Fig. 15. Thermal conductivity as function of the defect size: (a) Crush and (b) Sheath diameter.
0.04
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Fig. 18. Deterioration of the thermal resistance of the insulation according to defect type and size.
Fig. 19. Evolution of the expected energy consumption according to the thermal performance of the wall.
the heating and cooling degree-days2 [K], describing the local climate. Table 4 gathers the parameters used for this consumption and their values are drawn from the French market. As can be seen from Eqs. (9) and (10), three main parameters affect the energy consumption: 1. The thermal resistance of the wall (R), which is affected by the deterioration of the materials over time because of aging and moisture retaining, in addition to the impact of defects. 2. The heating degree days (HDD) and the cooling degree days (CDD) which depend on: a. Climate, describing the external environment of the wall. Over years, important climate changes are expected, resulting in decreasing heating needs and increasing cooling needs. b. Occupant comfort, governing the inside environment of the wall. This parameter fluctuates from an occupant to another. The total quantity of the annual heating and cooling degreedays is calculated by [31,39]
HDD ¼
HS X þ ðT base T 0 ðiÞÞ
ð11Þ
i¼1
CDD ¼
CD X þ ðT 0 ðiÞ T base Þ
ð12Þ
i¼1
2 The degree-days are generally used to simplify calculations of the annual energy consumption and it is commonly adopted by the building industry to relate trends of building energy consumption to local climate conditions. More details can be found in the works of Kaynakli [40], Aïssani et al. [31].
Parameters
Values
Materials
Thickness
Thermal conductivity
Hollow blocks Internal plaster External plaster Mineral wool
0.200 m 0.014 m 0.015 m 0.080 m
1.60 W/m K 0.32 W/m K 0.25 W/m K 0.35 W/m2 K
Length Width
1m 0.3 m
Fuel Heating efficiency
Electricity 0.6
Coef. of performance of the cooling climate
2.5 Region of ClermontFerrand
Life time (LT)
15 years
where Tbase is the base or reference temperature of the heated or cooled space (e.g. 18 °C in France, 18.5 °C in UK and 18.3 °C in USA), T0 is the average outdoor temperature of the ith day, HS is the average number of heating days and DS is the average number of cooling days. In this study, the climate corresponding to a cold region in France was considered. Over the years, the changes of the climate are still difficult to predict as they depend on many uncontrollable parameters [31]. In this study, it is assumed that the climate follows the worst scenario3 of global warming resulting in decreasing heating needs and increasing cooling needs. In Fig. 19, different predictions of the energy consumptions are presented in the case of walls considering non-defective or defective insulations. We can see that in all cases, the shape of the predictions is very similar. In early years, the total energy consumption is expected to increase due to the deterioration of the thermal resistance of the insulation and to cold winters (High HDD)/warm summers (low CDD). However, with time, the temperatures are expected to increase in winters, resulting in lower HDD/ higher CDD, which explains the observed stabilization of the consumptions. The lowest curve represents the evolution of the energy consumption expected in the case of a wall containing a healthy insulation material. In this case, the insulation performs as expected, and the energy consumption is only affected by variation in climate and the aging of the material. Other predictions presented in Fig. 19 shows the evolution of the energy consumption when considering this time, the insulations altered by the defects studied previously. From a global point of view, it can be clearly notice that the presence of any kind of defect increases the initial energy needs and the gap between the curves is accordingly increased due to the considered type of defect. Under perfect considerations, the energy consumption would be of 21 kW h/m2 annually, while the presence of a groove can lead to more than 3.5 kW h/m2 extra energy consumption per year, and the presence of the worst defect (opening) may lead to more than 15 kW h/m2 annually. Over time, when looking at the predictions associated to the healthy material (lowest curve), the deterioration of the insulation itself results in almost 9.5% in extra energy needs in less than 15 years. However, in presence of defects, it can be seen that the extra need in energy can reach 12% for a groove width defect of 4 cm, versus 19% for a groove depth of the same size; whereas a crushed material needs 23.8% in energy versus 47.6% for a material 3 According to the predictions of the intergovernmental Panel on Climate Change (IPCC).
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crossed by a sheath. Besides, the opening represents the worst defect as the presence of such defect makes the energy consumption 72% higher than the expected one for a defect-free insulation. Remark In case of warm climates, the evolution of the energy consumptions would be obviously lower, as the indoor environment would need less heating and cooling to ensure the comfort temperature. However, the presence of defect would also lead to high heat losses leading to higher energy demand and over time the energy consumption is also expected to increase due to the material deterioration and global warming. 8. Conclusion This paper investigates the impact of workmanship errors on the thermal performance of insulation and proposes analytical models to evaluate the effective thermal conductivity to use in simulation, according to the type and size of defect. Four common defects were studied. An experimental study permitted to evaluate the impact of the defects on the global thermal resistance. Each defect is then modeled using the finite element method. The inverse method allowed us to calibrate the finite element models using experimental results, to evaluate the global effective thermal conductivity representing the effective performance of the defective insulation. To control the energy consumption of buildings, it is important to consider all sources of uncertainty that may affect the predictions, since the early stages of design. For this purpose, this paper proposes two analytical models that can be used to quantify the effective thermal conductivity, in the case of regular defects such as grooves or openings on one hand, and in the case of irregular defects such as crushes and sheath passages on flexible materials, on the other hand. The impact of the defect type is compared and shows that the thermal performance of the insulation is more sensitive to irregular defects than to regular ones. A parametric study allowed us to highlight that openings are the worst defects that can affect the insulation. This is an important issue, as openings often appear over time especially with flexible materials. However, in the case of small sized defects, irregular defects are found to be more affecting the insulation resistance in contrast to grooves and small openings. The studied defects are more often found in walls insulations. A practical application allowed us to quantify the deviation of the energy consumptions, when considering the different defects, compared to the expected energy consumption of a healthy material. The results show that the energy consumption is highly influenced by the quality of the installation, regardless to indoor and outdoor environmental conditions. As a matter of fact, it was also noticed that the presence of openings resulted in the highest energy consumptions. The uncertainty analysis showed that the scatter of experimental results increases with larger amount of defect. The highest dispersions are observed for irregular defects that commonly affect flexible materials. Besides, the lognormal distribution appeared to fit well the experimental distribution of results. For the decision making purposes, the obtained statistical results provide useful information to use in the simulation process and for reliability analyses. 8.1. Practical recommendations This work showed that the thermal resistance of insulations was significantly affected by all kinds of defect. However, some defects appeared to be more harmful than others.
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As a matter of fact, all kinds of defects creating holes or openings must be avoided in practice, as they induce very important heat losses resulting in non-negligible extra energy consumptions. This paper showed that insulation panels were found to lose more than 36% of their performance when openings represent only 0.5% the total insulation volume. Besides, the losses increase exponentially when the defect volume increases. Openings are unacceptable defects and must be avoided at any time. In case of flexible materials, it is common to observe crushes in insulation panels. If the panel is crushed at 0.5% of its total volume, the insulation losses almost 12% of its initial performance. That means that, if we want to maintain the initial consumption predictions, the insulation should be oversized of at least 12%. In case of synthetic materials, construction site workers are used to perform grooves to make electrical wires and sheath standing on the walls. It is found that wide grooves are less harmful than deep grooves, as the insulation thicknesses are less affected with wide grooves. For a groove representing 0.5% of the total insulation volume, it is recommended to oversize the insulation of 40% if the groove is in depth versus 28% if the groove is in width. In addition, it was found that the performance of synthetic materials is more controlled. Indeed, even if the initial performance of polystyrene is expected to be lower than the one of fibrous materials, it seems that the performance scatter of such material is lower than fibrous materials. The proposed models may be helpful to set the maximum bearable workmanship defects. However, it could be interesting to conduct in situ measurements and consider expert judgments to set average defaults sizes commonly observed on construction sites, to be used in simulation. Hence, according to the type of the material, the type and size of defect, designers would be able to anticipate an average over sizing of the insulation. References [1] Al-Sanea S, Zedan M, Al-Hussain S. Effect of masonry material and surface absorptivity on critical thermal mass in insulated building walls. Appl Energy 2013;102:1063–70. [2] Martin K, Escudero C, Erkoreka A, Flores I, Sala J. Equivalent wall method for dynamic characterisation of thermal bridges. Energy Build 2012;55:704–14. [3] Bond D, Clark W, Kimber M. Configuring wall layers for improved insulation performance. Appl Energy 2013;112:235–45. [4] Abdou A, Budaiwi I. The variation of thermal conductivity of fibrous insulation materials under different levels of moisture content. Constr Build Mater 2013;43:533–44. [5] Wei S, Fu-chi W, Qun-Bo F, Zhuang M. Effects of defects on the effective thermal conductivity of thermal barrier coatings. Appl Math Model 2012;36:1995–2002. [6] Lu Y, Huang Z, Zhang T. Method and case study of quantitative uncertainty analysis in building energy consumption inventories. Energy Build 2014;57:2159–68. [7] Dominguez-Munoz F, Anderson B, Cejudo-Lopez J, Carrillo-Andrés A. Uncertainty in thermal conductivity of insulation materials. Energy Build 2010;42:2159–68. [8] Aïssani A, Chateauneuf A, Fontaine J-P, Audebert P. Cost model for optimum thicknesses of insulated walls considering indirect impacts and uncertainties. Energy Build 2014;84:21–32. [9] Spitz C, Mora L, Wurtz E, Jay A. Practical application of uncertainty analysis and sensitivity analysis on an experimental house. Energy Build 2012;55:459–70. [10] Hopfe C, Hensen J-L. Uncertainty analysis in building performance simulation for design support. Energy Build 2011;43:1010–8. [11] Asdrubali F, Baldinelli G, Bianchi F. A quantitative methodology to evaluate thermal bridges in buildings. Appl Energy 2012;97:365–73. [12] El Diasty R. Effect of thermal anomalies and dimensional instability on building envelopes performance. Constr Build Mater 1988;2. [13] C.E.c.f. standardization, ISO 10211:2007: thermal bridges in building construction – international standards, BSI British standards; 2007. [14] Sierra F, Bai J, Maksoud T. Impact of the simplification of the methodology used to assess the thermal bridge of the head of an opening. Energy Build 2015;87:342–7. [15] Ibrahim M, Biwole PH, Wurtz E, Achard P. Limiting windows offset thermal bridge losses using a new insulating coating. Appl Energy 2014;123:220–31. [16] Zalewski L, Lassue S, Rousse D, Boukhalfa K. Experimental and numerical characterization of thermal bridges in prefabricated building walls. Energy Convers Manage 2010;51:2869–77.
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