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Thermal performances of glazed energy storage systems with various storage materials: An experimental study
Sustainable Cities and Society 45 (2019) 422–430
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Thermal performances of glazed energy storage systems with various storage materials: An experimental study
T
⁎
Benjamin Duraković , Selma Mešetović International University of Sarajevo, Bosnia
A R T I C LE I N FO
A B S T R A C T
Keywords: Energy storage system Glazed heat exchanger Phase change material Temperature variation
In the literature, two materials (such as water and phase change materials) have been studied frequently as potential heat energy storage medium in building applications. Many perspectives have been mentioned by researchers but there is too little information about significance of its application. In this experimental study, water and PCM glazing systems were tested simultaneously in natural environment using test chambers. Temperature histories were recorded in the corresponding chambers at interior glass surface for each sample. Time lag, temperature damping and average temperature for each glazing system were comparatively analyzed. It was observed that temperature variation at interior glass surface over a period of 48 h changes differently for each material. Compared to the traditional air filled glazing system, it was found that water glazing system has most promising temperature damping properties, but significantly unfavorable average temperature, while PCM glazing system has significantly promising average temperature and temperature dumping properties.
1. Introduction Reducing energy demands for building heating and cooling is challenging but achievable. Various techniques and approaches were mentioned in the literature such as thermal insulation of building envelope by using new materials. Energy storage might be another way to reduce building energy demand by improving thermal inertia of its components (Hasan, Basher, & Shdhan, 2018). Also, various heat storage materials used in building passive thermal design have been discussed, but only three of them are generally recommended such as phase change materials (PCMs) (Torlak, Delalić, Duraković, & Gavranović, 2014), water (or water‒antifreeze mixture) and rocks. Each of these materials has a characteristic that might be a desirable under certain conditions. Rocks are cheap and readily available but have lower heat storage per unit of volume. Compared to rocks, water is also cheap, readily available and more efficient due to increased heat storage per unit of volume. PCMs have the highest energy storage potential due to latent heat during the phase transition process. Since PCMs have ability to store and release the heat during phase change processes by delaying heat gain to the interior (load shifting effects), they were subject of interest for the researches with the aim of improving thermal performances of buildings. Application of phase change material in building envelope for energy storage is a new approach in building energy demand reduction (Silva, Vicente, &
⁎
Rodrigues, 2016). PCMs use latent heat of melting and solidification to store relatively large amounts of energy that can be utilized later. The process of melting and solidification takes place at constant or narrow temperature ranges. Also, degradation may happen due to thermal large numbers of cycling (Benomar et al., 2015). PCMs can use variety of materials and can be classified as organic PCMs or inorganic PCMs. Although both share same latent heat per unit mass characteristics, however inorganic PCMs have higher latent heat per unit volume characteristic (Han & Taylor, 2016). Moreover, PCMs performance in a building structure depends on climate, design and building orientation (Madhumathi & Sundarraja, 2012). Application of PCMs in moderate climates may perform well, while the application of PCMs in tropical climates may face many challenges. Moderate climates need space heating tropical climate needs space cooling, thus north-south orientation as well as proper choice of PCMs are critical (Jiawei Lei, 2016). The PCMs can be applied into various building components, such as walls, windows, etc., as well as its usage spreads to specific building composition material such as plaster, concrete, brick (Nkwettak & Haghighat, 2014) or floor (Belmonte, Eguíac, Molina, & AlmendrosIbáñez, 2015). The PCMs application in building structure includes several ways, such as microencapsulate and microencapsulate PCMs solutions. Also, PCMs slurries are used as well with active systems in cases where heat transfer fluid is used (HTF). The slurry enhances the
Corresponding author. E-mail address: [email protected] (B. Duraković).
https://doi.org/10.1016/j.scs.2018.12.003 Received 16 January 2018; Received in revised form 4 December 2018; Accepted 5 December 2018 Available online 05 December 2018 2210-6707/ © 2018 Elsevier Ltd. All rights reserved.
Sustainable Cities and Society 45 (2019) 422–430
B. Duraković, S. Mešetović
energy transmittance but it can control solar heat gain by allowing energy harvesting (P. Sierra & Hernández, 2017). Also, solutions with buoyant water circulation in the glazed system showed capability of reducing energy consumption for cooling (Chow & Li, 2013). Experimental studies confirmed that temperature dumping was promising in water based system (Durakovic & Torlak, 2017b). The aim of this experimental study is to determine whether there are significant differences in the performance of these materials (PCM and water) applied in a glazing system and how significant they are. The needs for this study are reflected in the contribution to the material selection in the development of environmentally responsive building components (IEA International Energy Agency, 2011) based on the energy storage capabilities. Previously published work was mainly focused on application of storage materials in glazing systems and assessment of its performances. Too many perspectives of energy storage materials have been studied. Different materials have different performance, but there is no information about how much these performance differences are significant. Therefore, the study aims to answer some basic questions about the significance of variable material performances in glazing systems. In this regards a novel simplified concept of water based glazing system is introduced without forced circulation, which utilizes specific heat capacity for temperature damping. Generally, water-based glazing systems are used for glazing areas temperature lowering and as that, they might be applicable in hot climate areas only. Note, the glazed system concepts with circulating water mentioned in the literature might be more expensive and maintenance demanding. For the sake of simplification, the concept is based on the ability of water to absorb heat (as sensible) due to increased specific heat capacity of water, which is about 4.2 kJ/kgK. Thus, this study comparatively investigate the differences in thermal performance of the same type1 water filled glazed system with traditional air filled and PCM filled systems. The concept showed promising results and the differences are significant in favor of PCM and water.
heat storage in HTF (Soares, Costa, Gaspar, & Santos, 2013). Results available from previous studies show that PCMs usage in walls (plaster, concrete) can significantly decrease energy demand, while windows are set to be weakest building element in terms of heat loss. Several studies on PCM glazing system were conducted using experimental or numerical methods. The cycling processes on various PCM system configurations were conducted such as interior shading devices, exterior shading devices, and integrated glazing PCM energy storage system (Soares, Samagaio, Vicente, & Costa, 2011; Vigna, Bianco, Goia, & Serra, 2018). Interior PCM shading devices and exterior PCM shading devices were studied by experimental and numerical methods respectively. Thermal performance of an interior PCM system with horizontal and vertical blinds was reported as well (Mehling, 2005). On the other hand, many researchers conducted studies on numerical simulations of thermal performance of exterior PCM energy storage system by using finite element model, two dimensional simulation models, and wooden box having PCM storage system facing south. As a conclusion, the main shortcoming of the interior PCM storage systems is late penetration of heat to the interior while exterior PCM storage systems failure is high impermeability of light into interior space. Drawbacks of both systems were basis for research on integrated PCM energy storage glazing system. 1.1. Integrated energy storage glazing systems Several studies have been conducted on integrated energy storage glazing systems with the aim of reducing its surface temperature and preventing the glazed areas from overheating. Load shifting effects and thermal performances of PCM energy storage glazed systems in a hot climate regions have better performances (Zhong, Li, Sun, Zheng, & Zhang, 2015) while in cold climate regions the load shifting effect and thermal performances were reduced due to phase transition incompletion (Grynning, Goia, & Time, 2015). The load shifting and thermal performances of the system can be improved by increasing the thickness of the cavity between glazing panes or using triple glazed system (Li, Sun, Zou, & Zhang, 2016). The optimum cavity width for continental climate is about 24 mm and can provide 17 h of phase transition period by maintaining the temperature of the glazing system near 26 °C (Durakovic & Torlak, 2017a). Improving PCM thermo‒physical parameters such as latent heat and phase transition temperature in the range of 25–31 °C can improve load shifting and thermal performances of the glazed system (Li, Li et al., 2016). Optical properties of phase change materials are an important factor for thermal performance of a double glazed system. Particularly, the effect of PCM refractive index and extinction coefficient on the temperature and transmitted energy show that there is a weak impact of refractive index on the temperature while the impact of extinction coefficient is significant (Li, Ma et al., 2016). The significance of extinction coefficient depends of phase transition, e.g. the value of extinction coefficient decreases from 30 m−1 in fully solid phase to 4 m−1 in fully liquid phase (Gowreesunker, Stankovic, Tassou, & Kyriacou, 2013), while refractive index changes between 1.3 and 1.4 (Li, Ma et al., 2016). Also, experimental results confirm that transmittance of double glazes system with liquid phase is 0.5 (Li, Li et al., 2016; Liu, Wu, Zhu, Li, & Ma, 2018). Optical properties, translucence in solid phase and transparency in the liquid phase of the glazed system are acceptable but limited to the certain type of applications (Li, Li et al., 2016). Water as energy storage medium in a glazed solar heat exchanger was studied as part of remote water tank system for load shifting (Chow, Li, & Lin, 2011). The thermal characteristic and its annual performances were investigated (Li & Chow, 2011). Water flow through glazed cavity improves thermal inertia of the glazed system by absorbing infrared radiation and lowering interior glass temperature (Gonzalo & Ramos, 2016). In this way the flow rate does not affect
2. Research methods 2.1. Experimental method To conduct this analysis an experimental method is used while statistical tools were applied to analyze data obtained from the experiment. The setting includes parameters resembling a typical living room conditions. Experimental box including three chambers is built out of insulating material and exposed to the natural environment as it is shown in Fig. 1. Experimental setup uses a three chamber box to resemble living room state with windows facing south to maximize solar heat gain. The box is subdivided into three smaller chambers (Chamber #1, Chamber #2 and Chamber #3) in order to test three samples at the same time. Furthermore, the chamber is made out of 5 cm thick Styrofoam having dimensions width x length x height2 = 150 × 50 x 50 cm, while small chambers share same material property but differ in dimensions; each has a size of width x length x height = 50 × 50 x 50 cm. Glazing samples used in this experiment had dimensions of width x height = 30 x 41 cm, while the glass pane thickness was 4 mm each with the cavity space between panes of 12 mm. Based on the previous studies (Durakovic & Torlak, 2017a) it was proved that from 12 mm cavity sample, can be recorded quite enough good data for the comparative study in order to determine significant differences if they exist. Temperatures were recorded at interior glass surface (T4 temperatures) in chambers using Vernier surface temperature sensors that can measure temperature 1 Not different, because the same glazed system under the same boundary conditions will provide reliable results. 2 width (W); length (L); height (H)
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Fig. 1. Experimental setup graphical explanation.
range from –25 to 125 °C with LabQuest2 data logger. The accuracy of the sensor is up to ± 0.2 °C for the temperature range measured in this experiment. Weather conditions were: mostly clear sky with average air temperature during the day of 25 °C in summer3 and −4.5 °C in winter4; peak solar radiation on vertical plane was 530 W/m2 in summer and 680 W/m2 in winter, measured by Vernier pyranometer at P2 position for both seasons with ± 5% accuracy. Phase change material used for purpose of this study was RT27 paraffin from Rubitherm. The physical properties of RT27 paraffin are given in Table 1, while for comparisons the properties of air and water were taken from standard literature (VDI et al., 2010).
Table 1 Physical properties of materials under tested conditions (Rubitherm, 2015; VDI et al., 2010).
2.2. Statistical data analysis The temperatures recorded on the interior glass surface in a hot summer day were analyzed using statistical methods for variances and for means. To find significant difference in performances among these three materials, Analysis of Variance (ANOVA) and post-hoc t-tests were used. ANOVA technique helps to analyze the differences in temperature variation at the interior glass surface amongst different materials used in a glazing system (Durakovic, 2017). In this case, single factor ANOVA at 95% confidence interval is used to analyze performances among three materials. Typical data matrix is shown in Table 2. where, a is number of treatments (materials), n represents number of observations, yij represents? ?-th temperature observation taken for the material i . Initially it considers the case in which there are an equal number of temperature observations? ?, for each material. The observations from Table 2 can be represented with a linear statistical model.
i = 1,2, …, a yij = μ + τij + εij ⎧ ⎨ ⎩ j = 1,2, …, n
Name
RT 27
Water
Air
Density, gas Density, solid phase Density, liquid phase Dynamic viscosity, Specific heat (both phases) Thermal conductivity (both phases) Latent heat Solidus temperature Liquidus temperature
NA 880 kg/m3 760 kg/m3 0.02 Pa s 2 kJ/kgK 0.2 W/mK
NA NA 997 890.0 × 10−6 Pa s 4.18 kJ/kgK 0.6 W/mK
1.188 kg/m3 NA NA 1.85 × 10−5 Pa s 1.0064 kJ/kgK 0.026 W/mK
189 kJ/kg 24.5 °C 26.5 °C
NA NA NA
NA NA NA
Table 2 Typical data for a single factor experiment (Durakovic, 2017). Materials
Observations
1 2 . . a
y11 y21 . . ya1
y12 y22 . . ya2
… … … … …
y1n y2 . . yan
Totals
Averages
y1 ∙ y2 ∙ . . ya ∙ y∙∙
y¯1 ∙ y¯2 ∙ . . y¯a ∙ y¯∙∙
i = 1,2, …, a yij = μi + εij ⎧ ⎨ ⎩ j = 1,2, …, n
Total sum of squares represents sum of square from treatments (SSTr ) and error (SSE ) and can be calculated as:
SST = SSTr + SSE (1)
or
where yij is a random variable indicating n-th observation,? ? is the mean value, τi is an i-th treatment-related parameter called i-th treatment effect, εij and is a random error member. The model can be written as:
SST =
a
4
(3)
n
∑ ∑ (yij − y¯∙∙ )2 i=1 j=1
(4)
Where treatment sum of squares is defined as: a
SSTr = n∑ (y¯i ∙ − y¯∙∙ )2 3
(2)
i=1
Recorded in June 2017 - Sarajevo, Bosnia Recorded in December 2017 -Sarajevo, Bosnia
And error sum of squares formula is defined as: 424
(5)
Sustainable Cities and Society 45 (2019) 422–430
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SSE =
n
m2 at its peak. In winter season, the air temperature changed between −9 °C and + 6 °C, while the irradiance on vertical plane reaches 680 W/m2 at its peak. In Fig. 2 (d), the noise is observed over tested period, which is caused by clouds during some period of days. The results for tested samples for summer and winter case are shown in Figs. 3 and 5 respectively. Since water-based system might be applicable only in hot climate zones, such as tropic and subtropics climate zones, the boundary condition will be different. Generally, solar radiation and the exterior temperature are higher in these climate zones. Solar radiation on the vertical plane is reduced and depends of azimuth and elevation. Therefore based on available data (Photovoltaic Geographical Information System, 2017), for the south-oriented vertical surface (azimuth = 0 °), due to the increase of the solar elevation angle the component of directing solar radiation is reduced on the vertical plane while diffuse radiation and the average air temperatures increase with approaching to the Equator. For instance, the sum of direct and diffuse solar radiation in hot climate on the vertical plane is below the values measured in Sarajevo (Photovoltaic Geographical Information System, 2017). Due to the reduced incident solar radiation on the vertical plane, it is expected that the water will heat slowly and the PCM will melt slowly, and this will contribute to the thermal comfort in the room for longer period of time. The average values of exterior air temperature at its peak at noon may reach up to 42 °C, which may cause slightly different results at interior surface temperature. In this study experimental results from hot climate are not available, but it can be the subject of future research. The selected summer days are characterized by significant solar radiation. The idea is that thermal performance of systems are tested under extreme conditions of solar radiation. These conditions provide the shortest period of phase change and water heating. Since the system demonstrated significant differences in thermal performance under the extreme conditions it is expected to have better performance in case of exposure to weather conditions that are not extreme (the water will heat slowly and the phase transition will take more time). Also, the system was tested under partially cloudy day conditions (clouds afternoon in the second day), where the temperature change trend for all three systems were observed. The liquid phase of PCM and the air sample have a very similar response to the clouds, while water tries to maintain a higher temperature. Direct transmission of solar irradiance through the system is not measured in this experiment. The transmittance reported in the literature for water based glazing systems is 0.262(P. Sierra & Hernández, 2017). For PCM sample, transmittance depends of PCM phase state. For 86 mm thick glazing system it is reported for solid state between 0.08 and 0.28 and for the liquid state it is between 0.12 and 0.44. For the sample of 15 mm gap the transmittance was reported as of 0.56 (Ismail & Henrı́quez, 2002).
∑ ∑ (yij − yj∙ )2 (6)
i=1 j=1
Finally, observed F statistic represent the ration between mean sum of squares between treatment and mean sum of squared within the treatments (error) and can be calculated using the following formula:
Fo =
SSTr /(a − 1) MSTr = SSE /[a (n − 1)] MSE
(7)
where, (a − 1) represents degrees of freedom between materials, a (n − 1) represents degrees of freedom within the observations in the sample (error degrees of freedom), an − 1 represents total sum of squares. SSE represents error sum of squares, SSTr treatment sum of squares, MSTr is mean square of treatments and MSE is mean squares of error. Calculated Fo value is compared with critical value Fcr which was taken from statistical table for predetermined significance level of α = 0.05 and respecting the degrees of freedom of the numerator and the denominator in Eq. (7). If the calculated value of Fo from Eq. (7) is less than critical value taken from table (Fo > Fcr or p-value < α), it indicates that the model is statistically significant, otherwise significant differences do not exist. Since the results from the ANOVA provide overall model significance. This test indicates that there is at least one significant difference between the samples mean but it does not indicate which pairs differ significantly. To identify the differences for all pairs, post-hoc ttests comparisons were used as well. A two-sample t-test was used to indicate whether significant differences in mean performances exist between compared materials. The following cases were checked: μ1 ≠ μ 2 ; μ1 > μ 2 ; μ1 < μ 2 . where, μ1 and μ 2 represent the mean performances of material one and material two. Observed t-test statistics is calculated using unequal variance case as:
to =
x¯1 − x¯2 − Δ0 s12 n1
+
s 22 n2
(8)
where, x¯1 and x¯2 represent the mean temperature values for compared two materials respectively, Δo is hypothesized mean difference which is equal to zero, s1 and s2 are variances of material one and material two that are compared, while n1 and n2 represent number temperature records in compared materials one and two. If the calculated value of to from Eqs. (8), (7) is less than t-critical value taken from table (to > tcr or p-value < α), it indicates that there is significant difference in performance between two compared energy storage materials, otherwise significant differences do not exist. 3. Results and discussion
3.2. Summer thermal performances 3.1. Boundary conditions The experimental results of temperature variation between PCM and water compared to the air filled glazing system over 48 h for summer season are recoded and shown in Fig. 3. Fig. 3(c) shows temperature histories at interior glazing surface (in the chamber), which are represented with the different colors in the diagram. For PCM filled glazing system, the recorded temperature is represented with red color in the diagram, blue color represents recorded temperature at interior surface of a glazing system filled with water, and for the comparisons the green one represents the temperature record at interior surface of a conventional glazing system filled with air. It is observed that during cloudy sky between 30 and 33 h in Fig. 5, the temperatures on the interior glazing surface for air and PCM is more sensible than with water sample. The temperature trajectories are steeper than with water sample indicating that the cooling process of the pane is faster. Each of these three temperature records have
The boundary conditions of exterior air temperature and solar irradiance on the vertical plane (VP) for all three tested samples over 48 h, in summer and winter seasons are recorded and shown in Fig. 2. Fig. 2(a) and (b) represent boundary conditions for summer case of exterior air temperature and solar irradiance on vertical glazed surface during the experiment, while Fig. 2(c) and (d) represent boundary conditions in winter season for exterior air temperature and solar irradiance on vertical glazed surface. The experiment was conducted in late of June and December of 2017 in Sarajevo - Bosnia. The sky was clear without wind presence. In summer season, presence of cloud afternoon (Fig. 2(b)) was noticed in the second day of the experiment, which was recorded as noise in the diagram. The air temperature in was between + 7 °C and + 34 °C over the cycles, while the irradiance on vertical plane reached 530 W/ 425
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Fig. 2. Boundary conditions: exterior air temperature (a) summer and (c) winter; irradiance on VP (b) summer, (d) winter.
temperature variation and reduced radiation caused by clouds. The peak temperatures for each material are close even thought higher the PCM has higher thermal conductivity than water (see Table 1). Compared to the PCM and water glazing systems, air is considered as a good insulator due to its low heat conductivity but in presence of solar radiation, the traditional air glazing system has higher temperature variation and consequently higher impact to the heating /cooling load. In case of PCM and water glazed heat exchanger, overheating is reduced by accumulation of heat energy in these materials. The storage system can be optimized for the certain climate zone by adopting appropriate cavity thickness (Durakovic & Torlak, 2017a). The peak in traditional air glazing system has higher surface temperature for about 4 °C that PCM glazing system and 6 °C than water glazing system. PCM and water glazing systems have approximately three and one hour delay of temperature during the melting / heating process. The significance of these peak temperature differences are tested in using F-test and the results are shown in Table 4. The p-value is significant for each comparison indicating that damping properties of the water and PCM sample are significantly better compared to the air sample. Samples with lower variance in Table 4 indicate that temperature they have better damping properties. Comparative performances of these materials resulted from ANOVA at 95% confidence interval is shown in Table 5, where indicate that
Fig. 3. Histories of surface temperatures for water, PCM and air glazing system (summer case). Table 3 Correlation between temperature histories.
Water Air PCM
Water
Air
PCM
1 0.931 0.964
1 0.911
1
similar trends over the period; especially water and air filled glazing systems. Due to phase change process in PCM filled glazing system, the temperature history slope during phase transition period is decreased qualifying this system as most promising. The degrees of similarities are recorded in the correlation table, Table 3. As it was mentioned, water and the air glazing system have a similar trend with a correlation coefficient of 0.93 while PCM and the air have slightly decreased correlation coefficient of 0.911 due to phase transition period. It is observed that there are some daily variations in the temperature histories caused by variable weather conditions such as air
Table 4 Comparisons of temperature damping properties for summer case.
Mean Variance Observations df F P(F < =f) one-tail Fcr one-tail
426
Air
PCM
Air
H 2O
pcm
H 2O
26.3 225.9 41103 41102 1.38 7.2E-242 1.02
24.6 162.7 41103 41102
26.2 225.9 41103 41102 1.43 1.6E-293 1.02
26.7 157.3 41103 41102
24.6 162.7 41103 41102 1.03 0.0003 1.02
26.7 157.3 41103 41102
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Table 5 Single factor ANOVA results for summer case. ANOVA Source of Variation
SS
df
MS
Fo
P-value
F crit
Between materials Within material
100465.8 22439749
2 123306
50232.91 181.9842
276.02
2.5E-120
2.99
Total
22540215
123308
Table 6 Two-Sample t-test results for summer case. Material 1
Material 2
Mean (oC)
Variance
Significant (p‒value)
Fig. 5. Temperature histories for air, water and PCM glazing system (winter case).
Air
Water PCM Air Water
26.7 24.6 26.3 26.7
157.3 162.7 225.9 157.3
1.1E-05* 0* 0* 0*
3.3. Winter thermal performances
PCM
The experimental results of temperature variation at interior glass surface between PCM and air filled glazing system over 48 h for winter season are recoded and shown in Fig. 5. Since these record were done in winter season water sample was out of consideration due to its freezing point at 0 °C. Referring to Fig. 5, red and blue colors in the diagram represents temperature histories of PCM and air filled glazing system respectively. Conventional air filled sample was more sensible to solar irradiance variation than PCM sample. PCM sample due to energy stored as latent heat during melting are capable of keeping the temperature of the glazing above 20 °C and contributing to space heating over 10 h period. After the temperature falls below 20 °C the sample start to take heat from the space but temperature difference between room temperature and the PCM system is more favorable than with the air sample. The results of thermal performance analysis of these two materials are given in Table 7. From the data analysis results in Table 7, it is observed that the variance and the average temperature differ significantly, which is explained by F and t-tests. For F-test observed value is 1.8 which is greater than its Fcr = 1.14. It tells that the variance of the air sample is significantly higher the variance of PCM sample, which means that temperature damping properties of the PCM are significantly better in the winter season as well. Based on t-test results, observed t-value is 5.29, which is higher than its critical value tcr = 1.96, thus it can be claimed that the difference between average temperatures of the air and the PCM is significant in winter season as well. In another words, the PCM has significantly higher average temperature (10.7 °C) compared to the air sample (5 °C), which means it had better thermal properties in terms of energy dissipation in winter. With the aim of assessing experimental error impact on the results for both seasons, another analysis was done including temperature
* significant at p < 0.0167.
there is at least one significant difference. From ANOVA table, F and p‒values indicate that performances of at least one material differ significantly. Also, from F-test it is observed that there is a significant difference among temperature history variances for each material. Since, significant difference is observed for at least one material, post hoc t-test is applied to indicate which mean temperature differs significantly and the results are shown in Table 6 and Fig. 4. Referring to Fig. 4 and Table 6, it is observed that p ‒value is much lower than significance level α, thus there is strong evidence that significant difference among materials applied in a glazing system exists. The water sample variance value of 157.3 is the lowest one, which indicates a good temperature damping properties of this glazing system by ranking this sample as number one. A high average temperature value of 26.7 °C makes water sample as unfavorable. The PCM sample variance value of 162.7 is slightly higher than the one observed with water sample, which place this sample at number two in the rank of temperature dumping. This variance value indicates a good temperature damping properties as well, which are significantly better than the traditional air sample. Observed average temperature of the PCM sample was 26.3 °C, which is slightly lower than the one observed with the air sample. Since PCM sample has the property to absorb energy during the phase change (latent heat), delayed heat gain/loss was observed which is beneficial for temperature dumping time. Combining these three properties (variance, average temperature and temperature dumping time) of the PCM glazed system; it was observed that PCM sample had good performances for each of them and as result the temperature stayed stable for longer period compared to the other samples.
Fig. 4. Mean temperature and variance - a graphical representation. 427
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sensor accuracy range. The results showed that there is no change in average temperature values, thus the significant differences concluded above were confirmed. Temperature amplitudes were negligibly changed but significant results were confirmed again, which means that experimental error does not affect the measurement.
Table 7 PCM vs. air sample comparative F and t-test results. Materials
Mean (oC)
Variance
PCM Air
10.7 5.0
237.1 428.7
F-test t-test
Observed 1.80 5.29
Critical 1.14 1.96
Significance (p‒value) 8.6E-13* 7.1E-08*
3.4. Heat exchange between glazing system and indoor air Heat gain/loss from the system to the room is function of the surface temperature. Radiation component and convection components of heat
* significant at p < 0.01.
Fig. 6. Sensitivity analysis of heat transfer between glazing system and room for = 4; 5 and 6 W/m2K ; (S - summer; W - winter). 428
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Fig. 7. Comparison of total heat exchange for each material per season (S - summer; W - winter). Table 8 Result summary of comparative analysis. Winter
PCM tot
Air tot
Sign.
Mean Variance
−69.23 16813.59
−132.69 33323.75
Yes* Yes*
Summer Mean Variance
PCM 4.643024 17,119.91
Air 23.32046 24184.98
Sign. **
Yes Yes**
H2O
Air
25.86258 16,589.55
23.32046 24184.98
Sign. **
Yes Yes**
H2O
PCM
Sign.
25.86258 16,589.55
4.643024 17,119.91
Yes** Yes**
* Significant at p = 0.05. ** Significant at p = 0.017.
procedure explained earlier for thermal performances analysis was done and it was observed that significant differences exist in heat exchange between the room and glazed system following the conclusions earlier drawn with temperature analysis. This is expected because the heat transfer is function of the surface temperature. The result summary is shown in Bases on Table 8, each comparison has significant p-value for variances and mean heat exchange indicate significant differences in the thermal behavior, which is confirmed with ANOVA and post hoc t-tests, while F-tests confirmed significant difference between amplitudes of the heat transfer.
gain/loss to the room for each material is shown in Fig. 6. Sensitivity analysis for convection component of the heat exchange is performed using three values of convective heat exchange coefficient, which are adopted in the vicinity of the recommended values for test cells (Goia & Serra, 2018). The data in Figs. 6 and 7 are obtained by simulation. Component of 4 4 − Troom ), radiative heat transfer rate is obtained using QR = ε (Tsurf where ε is emissivity of the glass surface. Convective component is obtained using Qconv = α (Tsurf − Troom) , where α is convective heat transfer coefficient. Values for emissivity were taken from standard literature (Troom = 297.15 K for summer case; and Troom = 293.15 K for winter case; ε = 0.84) (ASHRAE, 2009), while convective heat transfer coefficient at interior glazing surface for test cell settings are recommended α = 4 to 5 W/m2K for summer case; α = 4.5 W/m2K for winter case (Goia & Serra, 2018), but for this study adopted values for the purpose of sensitivity analysis are 4; 5; and 6 W/m2K . Generally, increase of the convective heat transfer coefficient value from 4 to 6 W/m2K caused a slight increase in heat exchange between the glazed surface and interior environment. The convective heat exchange is less active during the night in summer for each value of the coefficient, which is expected due to lower temperature difference. In winter, the impact of convective heat transfer coefficient on the heat exchange is notable over the night due to a higher temperature difference between glazed surface and the room. Total heat exchange for each material was calculated as sum of these two Qtot = QR + Qconv and displayed in Fig. 7. For winter season it is obvious the significant differences in the heat exchange exists between air-based sample and PCM-based sample. The PCM saves more heat by delaying the rapid heat loss from the room for about 4.5 h. In summer season the case is similar, the PCM sample delays heat gain and rapid heat loss to/from the room for about 5 h in total, while the water-based sample shown potential in delaying of the heat gain/loss as well. Overall thermal performance these three materials were analyzed based on α = 5 W/m2K . Applying the same
4. Conclusion As a result of this study it was observed that there are very high similarities between water and the air glazing systems temperature trends with a correlation coefficient of 0.93 while PCM has slightly lower correlation due to phase transition period. Compared to a conventional glazing system, water and PCM filled glazing systems are significantly different. Water filled glazing system has increased specific heat capacity of water by resulting in one hour time lag. Statistically, it is observed that temperature damping is significant but average temperature is significantly higher. PCM filled glazing system has statistically significant temperature dumping over three hour period during melting process and four hour period during solidification process. Total temperature dumping period was about seven hours within temperature range of 24–28 °C. Also statistically speaking, the peak temperatures and average temperatures are significantly lower. References ASHRAE (2009). Handbook of fundamentals. USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Belmonte, J. F., Eguíac, P., Molina, A. E., & Almendros-Ibáñez, J. A. (2015). Thermal
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B. Duraković, S. Mešetović simulation and system optimization of a chilled ceiling coupled with a floor containing a phase change material (PCM). Sustainable Cities and Society, 15(February), 154–170. Benomar, W., Zennouhi, H., Arid, A., Rhafiki, T. E., Msaad, A. A., & Kousksou, T. (2015). PCM building material under cyclic melting and freezing processes. Journal of Materials and Environmental Science, 6(no. 12), 3416–3422. Chow, T.-T., & Li, C. (2013). Liquid-filled solar glazing design for buoyant water-flow. Building and Environment, 60(February), 45–55. Chow, T.-T., Li, C., & Lin, Z. (2011). Thermal characteristics of water-flow double-pane window. International Journal of Thermal Sciences, 50(no. 2), 140–148. Durakovic, B. (2017). Design of experiments application, concepts, examples: State of the art. Periodicals of Engineering and Natural Sciences, 5(no. 3), 421–439. Durakovic, B., & Torlak, M. (2017a). Experimental and numerical study of a PCM window model as a thermal energy storage unit. International Journal of Low-Carbon Technologies, 12(3), 272–280. Durakovic, B., & Torlak, M. (2017b). Simulation and experimental validation of phase change material and water used as heat storage medium in window applications. Journal of Materials and Environmental Sciences, 8(no. 5), 1837–1846. Goia, F., & Serra, V. (2018). Analysis of a non-calorimetric method for assessment of insitu thermal transmittance and solar factor of glazed systems. Solar Energy, 166(May), 458–471. Gonzalo, F. A., & Ramos, J. A. H. (2016). Testing of water flow glazing in shallow geothermal systems. Procedia Engineering, 161, 887–891. Gowreesunker, B. L., Stankovic, S. B., Tassou, S. A., & Kyriacou, P. A. (2013). Experimental and numerical investigations of the optical and thermal aspects of a PCM-glazed unit. Energy and Buildings, 61, 239–249. Grynning, S., Goia, F., & Time, B. (2015). Dynamic thermal performance of a PCM window system: Characterization using large scale measurements. Energy Procedia, 78, 85–90. Han, Y., & Taylor, J. E. (2016). Simulating the inter-building effect on energy consumption from embedding phase change materials in building envelopes. Sustainable Cities and Society, 27(November), 287–295. Hasan, M. I., Basher, H. O., & Shdhan, A. O. (2018). Experimental investigation of phase change materials for insulation of residential buildings. Sustainable Cities and Society, 36(January), 42–58. IEA International Energy Agency (2011). Annex 44 integrating environmentally responsive elements in buildings," IEA International Energy Agency, Energy Conservation in buildings & community systems. [Online]. Available: . [Accessed 15 January 2018] http://www. ecbcs.org/annexes/annex44.htm. Ismail, K., & Henrı́quez, J. (2002). Parametric study on composite and PCM glass systems. Energy Conversion and Management, 43(no. 7), 973–993. Jiawei Lei, J. Y. E.-H. Y. (2016). Energy performance of building envelopes integrated with phase change materials for cooling load reduction in tropical Singapore. Applied Energy, 162(January (15)), 207–217. Li, C., & Chow, T.-t. (2011). Water-filled double reflective window and its year-round performance. Procedia Environmental Sciences, 11(Part B), 1039–1047.
Li, D., Li, Z., Zheng, Y., Liu, C., Hussein, A. K., & Liu, X. (2016). Thermal performance of a PCM-filled double-glazing unit with different thermophysical parameters of PCM. Solar Energy, 133, 207–220. Li, D., Ma, T., Liu, C., Zheng, Y., Wang, Z., & Liu, X. (2016). Thermal performance of a PCM-filled double glazing unit with different optical properties of phase change material. Energy and Buildings, 119(May), 143–152. Li, S., Sun, G., Zou, K., & Zhang, X. (2016). Experimental research on the dynamic thermal performance of a novel triple-pane building window filled with PCM. Sustainable Cities and Society, 27(November), 15–22. Liu, C., Wu, Y., Zhu, Y., Li, D., & Ma, L. (2018). Experimental investigation of optical and thermal performance of a PCM-glazed unit for building applications. Energy and Buildings, 158(January), 794–800. Madhumathi, A. A., & Sundarraja, M. C. (2012). Experimental study of passive cooling of building facade using phase change materials to increase thermal comfort in buildings in hot humid areas. International Journal of Energy and Environment. Mehling, H. (2005). Strategic project ‘Innovative PCM-technology-results and future perspectives. 8th Expert Meeting and Workshop. Nkwettak, D. N., & Haghighat, F. (2014). Thermal energy storage with phase change material—A state-of-the art review. Sustainable Cities and Society, 10(February), 87–100. Sierra, P., & Hernández, J. A. (2017). Solar heat gain coefficient of water flow glazings. Energy and Buildings, 139(March), 133–145. Photovoltaic Geographical Information System (2017). Photovoltaic geographical information system. [Online]. Available: European Commission. [Accessed 20 11 2018] http://re.jrc.ec.europa.eu/pvg_tools/en/tools.html. Rubitherm (2015). "ORGANIC PCM – RT," Rubitherm. November [Online]. Available:http://www.rubitherm.eu/. Silva, T., Vicente, R., & Rodrigues, F. (2016). Literature review on the use of phase change materials in glazing and shading solutions. Renewable and Sustainable Energy Reviews, 53(January), 515–535. Soares, N., Costa, J., Gaspar, A., & Santos, P. (2013). Review of passive PCM latent heat thermal energy storage systems towards buildings’ energy efficiency. Energy and Buildings, 59(April), 82–103. Soares, N., Samagaio, A., Vicente, R., & Costa, J. (2011). Numerical simulation of a PCM shutter for buildings space heating during the winter. Proceedings of WREC – World Renewable Energy Congress. Torlak, M., Delalić, N., Duraković, B., & Gavranović, H. (2014). CFD-based assessment of thermal energy storage in phase-change materials (PCM). Energy technologies conference ENTECH ‘14 Istanbul, Turkey, December 22-24. VDI, VDI Heat Atlas, G. V. u. C. VDI, Ed. Dusseldorf: Springer Berlin Heidelberg, 2010. Vigna, I., Bianco, L., Goia, F., & Serra, V. (2018). Phase change materials in transparent building envelopes: A strengths, weakness, opportunities and threats (SWOT) analysis. Energies, 11(1), 111. https://doi.org/10.3390/en11010111 2018. Zhong, K., Li, S., Sun, G., Zheng, J., & Zhang, X. (2015). Simulation study on effects of phase change material thermal parameters on dynamic heat transfer performance of PCM-filled glass window. 6th International Building Physics Conference.
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Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs
Thermal performances of glazed energy storage systems with various storage materials: An experimental study
T
⁎
Benjamin Duraković , Selma Mešetović International University of Sarajevo, Bosnia
A R T I C LE I N FO
A B S T R A C T
Keywords: Energy storage system Glazed heat exchanger Phase change material Temperature variation
In the literature, two materials (such as water and phase change materials) have been studied frequently as potential heat energy storage medium in building applications. Many perspectives have been mentioned by researchers but there is too little information about significance of its application. In this experimental study, water and PCM glazing systems were tested simultaneously in natural environment using test chambers. Temperature histories were recorded in the corresponding chambers at interior glass surface for each sample. Time lag, temperature damping and average temperature for each glazing system were comparatively analyzed. It was observed that temperature variation at interior glass surface over a period of 48 h changes differently for each material. Compared to the traditional air filled glazing system, it was found that water glazing system has most promising temperature damping properties, but significantly unfavorable average temperature, while PCM glazing system has significantly promising average temperature and temperature dumping properties.
1. Introduction Reducing energy demands for building heating and cooling is challenging but achievable. Various techniques and approaches were mentioned in the literature such as thermal insulation of building envelope by using new materials. Energy storage might be another way to reduce building energy demand by improving thermal inertia of its components (Hasan, Basher, & Shdhan, 2018). Also, various heat storage materials used in building passive thermal design have been discussed, but only three of them are generally recommended such as phase change materials (PCMs) (Torlak, Delalić, Duraković, & Gavranović, 2014), water (or water‒antifreeze mixture) and rocks. Each of these materials has a characteristic that might be a desirable under certain conditions. Rocks are cheap and readily available but have lower heat storage per unit of volume. Compared to rocks, water is also cheap, readily available and more efficient due to increased heat storage per unit of volume. PCMs have the highest energy storage potential due to latent heat during the phase transition process. Since PCMs have ability to store and release the heat during phase change processes by delaying heat gain to the interior (load shifting effects), they were subject of interest for the researches with the aim of improving thermal performances of buildings. Application of phase change material in building envelope for energy storage is a new approach in building energy demand reduction (Silva, Vicente, &
⁎
Rodrigues, 2016). PCMs use latent heat of melting and solidification to store relatively large amounts of energy that can be utilized later. The process of melting and solidification takes place at constant or narrow temperature ranges. Also, degradation may happen due to thermal large numbers of cycling (Benomar et al., 2015). PCMs can use variety of materials and can be classified as organic PCMs or inorganic PCMs. Although both share same latent heat per unit mass characteristics, however inorganic PCMs have higher latent heat per unit volume characteristic (Han & Taylor, 2016). Moreover, PCMs performance in a building structure depends on climate, design and building orientation (Madhumathi & Sundarraja, 2012). Application of PCMs in moderate climates may perform well, while the application of PCMs in tropical climates may face many challenges. Moderate climates need space heating tropical climate needs space cooling, thus north-south orientation as well as proper choice of PCMs are critical (Jiawei Lei, 2016). The PCMs can be applied into various building components, such as walls, windows, etc., as well as its usage spreads to specific building composition material such as plaster, concrete, brick (Nkwettak & Haghighat, 2014) or floor (Belmonte, Eguíac, Molina, & AlmendrosIbáñez, 2015). The PCMs application in building structure includes several ways, such as microencapsulate and microencapsulate PCMs solutions. Also, PCMs slurries are used as well with active systems in cases where heat transfer fluid is used (HTF). The slurry enhances the
Corresponding author. E-mail address: [email protected] (B. Duraković).
https://doi.org/10.1016/j.scs.2018.12.003 Received 16 January 2018; Received in revised form 4 December 2018; Accepted 5 December 2018 Available online 05 December 2018 2210-6707/ © 2018 Elsevier Ltd. All rights reserved.
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energy transmittance but it can control solar heat gain by allowing energy harvesting (P. Sierra & Hernández, 2017). Also, solutions with buoyant water circulation in the glazed system showed capability of reducing energy consumption for cooling (Chow & Li, 2013). Experimental studies confirmed that temperature dumping was promising in water based system (Durakovic & Torlak, 2017b). The aim of this experimental study is to determine whether there are significant differences in the performance of these materials (PCM and water) applied in a glazing system and how significant they are. The needs for this study are reflected in the contribution to the material selection in the development of environmentally responsive building components (IEA International Energy Agency, 2011) based on the energy storage capabilities. Previously published work was mainly focused on application of storage materials in glazing systems and assessment of its performances. Too many perspectives of energy storage materials have been studied. Different materials have different performance, but there is no information about how much these performance differences are significant. Therefore, the study aims to answer some basic questions about the significance of variable material performances in glazing systems. In this regards a novel simplified concept of water based glazing system is introduced without forced circulation, which utilizes specific heat capacity for temperature damping. Generally, water-based glazing systems are used for glazing areas temperature lowering and as that, they might be applicable in hot climate areas only. Note, the glazed system concepts with circulating water mentioned in the literature might be more expensive and maintenance demanding. For the sake of simplification, the concept is based on the ability of water to absorb heat (as sensible) due to increased specific heat capacity of water, which is about 4.2 kJ/kgK. Thus, this study comparatively investigate the differences in thermal performance of the same type1 water filled glazed system with traditional air filled and PCM filled systems. The concept showed promising results and the differences are significant in favor of PCM and water.
heat storage in HTF (Soares, Costa, Gaspar, & Santos, 2013). Results available from previous studies show that PCMs usage in walls (plaster, concrete) can significantly decrease energy demand, while windows are set to be weakest building element in terms of heat loss. Several studies on PCM glazing system were conducted using experimental or numerical methods. The cycling processes on various PCM system configurations were conducted such as interior shading devices, exterior shading devices, and integrated glazing PCM energy storage system (Soares, Samagaio, Vicente, & Costa, 2011; Vigna, Bianco, Goia, & Serra, 2018). Interior PCM shading devices and exterior PCM shading devices were studied by experimental and numerical methods respectively. Thermal performance of an interior PCM system with horizontal and vertical blinds was reported as well (Mehling, 2005). On the other hand, many researchers conducted studies on numerical simulations of thermal performance of exterior PCM energy storage system by using finite element model, two dimensional simulation models, and wooden box having PCM storage system facing south. As a conclusion, the main shortcoming of the interior PCM storage systems is late penetration of heat to the interior while exterior PCM storage systems failure is high impermeability of light into interior space. Drawbacks of both systems were basis for research on integrated PCM energy storage glazing system. 1.1. Integrated energy storage glazing systems Several studies have been conducted on integrated energy storage glazing systems with the aim of reducing its surface temperature and preventing the glazed areas from overheating. Load shifting effects and thermal performances of PCM energy storage glazed systems in a hot climate regions have better performances (Zhong, Li, Sun, Zheng, & Zhang, 2015) while in cold climate regions the load shifting effect and thermal performances were reduced due to phase transition incompletion (Grynning, Goia, & Time, 2015). The load shifting and thermal performances of the system can be improved by increasing the thickness of the cavity between glazing panes or using triple glazed system (Li, Sun, Zou, & Zhang, 2016). The optimum cavity width for continental climate is about 24 mm and can provide 17 h of phase transition period by maintaining the temperature of the glazing system near 26 °C (Durakovic & Torlak, 2017a). Improving PCM thermo‒physical parameters such as latent heat and phase transition temperature in the range of 25–31 °C can improve load shifting and thermal performances of the glazed system (Li, Li et al., 2016). Optical properties of phase change materials are an important factor for thermal performance of a double glazed system. Particularly, the effect of PCM refractive index and extinction coefficient on the temperature and transmitted energy show that there is a weak impact of refractive index on the temperature while the impact of extinction coefficient is significant (Li, Ma et al., 2016). The significance of extinction coefficient depends of phase transition, e.g. the value of extinction coefficient decreases from 30 m−1 in fully solid phase to 4 m−1 in fully liquid phase (Gowreesunker, Stankovic, Tassou, & Kyriacou, 2013), while refractive index changes between 1.3 and 1.4 (Li, Ma et al., 2016). Also, experimental results confirm that transmittance of double glazes system with liquid phase is 0.5 (Li, Li et al., 2016; Liu, Wu, Zhu, Li, & Ma, 2018). Optical properties, translucence in solid phase and transparency in the liquid phase of the glazed system are acceptable but limited to the certain type of applications (Li, Li et al., 2016). Water as energy storage medium in a glazed solar heat exchanger was studied as part of remote water tank system for load shifting (Chow, Li, & Lin, 2011). The thermal characteristic and its annual performances were investigated (Li & Chow, 2011). Water flow through glazed cavity improves thermal inertia of the glazed system by absorbing infrared radiation and lowering interior glass temperature (Gonzalo & Ramos, 2016). In this way the flow rate does not affect
2. Research methods 2.1. Experimental method To conduct this analysis an experimental method is used while statistical tools were applied to analyze data obtained from the experiment. The setting includes parameters resembling a typical living room conditions. Experimental box including three chambers is built out of insulating material and exposed to the natural environment as it is shown in Fig. 1. Experimental setup uses a three chamber box to resemble living room state with windows facing south to maximize solar heat gain. The box is subdivided into three smaller chambers (Chamber #1, Chamber #2 and Chamber #3) in order to test three samples at the same time. Furthermore, the chamber is made out of 5 cm thick Styrofoam having dimensions width x length x height2 = 150 × 50 x 50 cm, while small chambers share same material property but differ in dimensions; each has a size of width x length x height = 50 × 50 x 50 cm. Glazing samples used in this experiment had dimensions of width x height = 30 x 41 cm, while the glass pane thickness was 4 mm each with the cavity space between panes of 12 mm. Based on the previous studies (Durakovic & Torlak, 2017a) it was proved that from 12 mm cavity sample, can be recorded quite enough good data for the comparative study in order to determine significant differences if they exist. Temperatures were recorded at interior glass surface (T4 temperatures) in chambers using Vernier surface temperature sensors that can measure temperature 1 Not different, because the same glazed system under the same boundary conditions will provide reliable results. 2 width (W); length (L); height (H)
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Fig. 1. Experimental setup graphical explanation.
range from –25 to 125 °C with LabQuest2 data logger. The accuracy of the sensor is up to ± 0.2 °C for the temperature range measured in this experiment. Weather conditions were: mostly clear sky with average air temperature during the day of 25 °C in summer3 and −4.5 °C in winter4; peak solar radiation on vertical plane was 530 W/m2 in summer and 680 W/m2 in winter, measured by Vernier pyranometer at P2 position for both seasons with ± 5% accuracy. Phase change material used for purpose of this study was RT27 paraffin from Rubitherm. The physical properties of RT27 paraffin are given in Table 1, while for comparisons the properties of air and water were taken from standard literature (VDI et al., 2010).
Table 1 Physical properties of materials under tested conditions (Rubitherm, 2015; VDI et al., 2010).
2.2. Statistical data analysis The temperatures recorded on the interior glass surface in a hot summer day were analyzed using statistical methods for variances and for means. To find significant difference in performances among these three materials, Analysis of Variance (ANOVA) and post-hoc t-tests were used. ANOVA technique helps to analyze the differences in temperature variation at the interior glass surface amongst different materials used in a glazing system (Durakovic, 2017). In this case, single factor ANOVA at 95% confidence interval is used to analyze performances among three materials. Typical data matrix is shown in Table 2. where, a is number of treatments (materials), n represents number of observations, yij represents? ?-th temperature observation taken for the material i . Initially it considers the case in which there are an equal number of temperature observations? ?, for each material. The observations from Table 2 can be represented with a linear statistical model.
i = 1,2, …, a yij = μ + τij + εij ⎧ ⎨ ⎩ j = 1,2, …, n
Name
RT 27
Water
Air
Density, gas Density, solid phase Density, liquid phase Dynamic viscosity, Specific heat (both phases) Thermal conductivity (both phases) Latent heat Solidus temperature Liquidus temperature
NA 880 kg/m3 760 kg/m3 0.02 Pa s 2 kJ/kgK 0.2 W/mK
NA NA 997 890.0 × 10−6 Pa s 4.18 kJ/kgK 0.6 W/mK
1.188 kg/m3 NA NA 1.85 × 10−5 Pa s 1.0064 kJ/kgK 0.026 W/mK
189 kJ/kg 24.5 °C 26.5 °C
NA NA NA
NA NA NA
Table 2 Typical data for a single factor experiment (Durakovic, 2017). Materials
Observations
1 2 . . a
y11 y21 . . ya1
y12 y22 . . ya2
… … … … …
y1n y2 . . yan
Totals
Averages
y1 ∙ y2 ∙ . . ya ∙ y∙∙
y¯1 ∙ y¯2 ∙ . . y¯a ∙ y¯∙∙
i = 1,2, …, a yij = μi + εij ⎧ ⎨ ⎩ j = 1,2, …, n
Total sum of squares represents sum of square from treatments (SSTr ) and error (SSE ) and can be calculated as:
SST = SSTr + SSE (1)
or
where yij is a random variable indicating n-th observation,? ? is the mean value, τi is an i-th treatment-related parameter called i-th treatment effect, εij and is a random error member. The model can be written as:
SST =
a
4
(3)
n
∑ ∑ (yij − y¯∙∙ )2 i=1 j=1
(4)
Where treatment sum of squares is defined as: a
SSTr = n∑ (y¯i ∙ − y¯∙∙ )2 3
(2)
i=1
Recorded in June 2017 - Sarajevo, Bosnia Recorded in December 2017 -Sarajevo, Bosnia
And error sum of squares formula is defined as: 424
(5)
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SSE =
n
m2 at its peak. In winter season, the air temperature changed between −9 °C and + 6 °C, while the irradiance on vertical plane reaches 680 W/m2 at its peak. In Fig. 2 (d), the noise is observed over tested period, which is caused by clouds during some period of days. The results for tested samples for summer and winter case are shown in Figs. 3 and 5 respectively. Since water-based system might be applicable only in hot climate zones, such as tropic and subtropics climate zones, the boundary condition will be different. Generally, solar radiation and the exterior temperature are higher in these climate zones. Solar radiation on the vertical plane is reduced and depends of azimuth and elevation. Therefore based on available data (Photovoltaic Geographical Information System, 2017), for the south-oriented vertical surface (azimuth = 0 °), due to the increase of the solar elevation angle the component of directing solar radiation is reduced on the vertical plane while diffuse radiation and the average air temperatures increase with approaching to the Equator. For instance, the sum of direct and diffuse solar radiation in hot climate on the vertical plane is below the values measured in Sarajevo (Photovoltaic Geographical Information System, 2017). Due to the reduced incident solar radiation on the vertical plane, it is expected that the water will heat slowly and the PCM will melt slowly, and this will contribute to the thermal comfort in the room for longer period of time. The average values of exterior air temperature at its peak at noon may reach up to 42 °C, which may cause slightly different results at interior surface temperature. In this study experimental results from hot climate are not available, but it can be the subject of future research. The selected summer days are characterized by significant solar radiation. The idea is that thermal performance of systems are tested under extreme conditions of solar radiation. These conditions provide the shortest period of phase change and water heating. Since the system demonstrated significant differences in thermal performance under the extreme conditions it is expected to have better performance in case of exposure to weather conditions that are not extreme (the water will heat slowly and the phase transition will take more time). Also, the system was tested under partially cloudy day conditions (clouds afternoon in the second day), where the temperature change trend for all three systems were observed. The liquid phase of PCM and the air sample have a very similar response to the clouds, while water tries to maintain a higher temperature. Direct transmission of solar irradiance through the system is not measured in this experiment. The transmittance reported in the literature for water based glazing systems is 0.262(P. Sierra & Hernández, 2017). For PCM sample, transmittance depends of PCM phase state. For 86 mm thick glazing system it is reported for solid state between 0.08 and 0.28 and for the liquid state it is between 0.12 and 0.44. For the sample of 15 mm gap the transmittance was reported as of 0.56 (Ismail & Henrı́quez, 2002).
∑ ∑ (yij − yj∙ )2 (6)
i=1 j=1
Finally, observed F statistic represent the ration between mean sum of squares between treatment and mean sum of squared within the treatments (error) and can be calculated using the following formula:
Fo =
SSTr /(a − 1) MSTr = SSE /[a (n − 1)] MSE
(7)
where, (a − 1) represents degrees of freedom between materials, a (n − 1) represents degrees of freedom within the observations in the sample (error degrees of freedom), an − 1 represents total sum of squares. SSE represents error sum of squares, SSTr treatment sum of squares, MSTr is mean square of treatments and MSE is mean squares of error. Calculated Fo value is compared with critical value Fcr which was taken from statistical table for predetermined significance level of α = 0.05 and respecting the degrees of freedom of the numerator and the denominator in Eq. (7). If the calculated value of Fo from Eq. (7) is less than critical value taken from table (Fo > Fcr or p-value < α), it indicates that the model is statistically significant, otherwise significant differences do not exist. Since the results from the ANOVA provide overall model significance. This test indicates that there is at least one significant difference between the samples mean but it does not indicate which pairs differ significantly. To identify the differences for all pairs, post-hoc ttests comparisons were used as well. A two-sample t-test was used to indicate whether significant differences in mean performances exist between compared materials. The following cases were checked: μ1 ≠ μ 2 ; μ1 > μ 2 ; μ1 < μ 2 . where, μ1 and μ 2 represent the mean performances of material one and material two. Observed t-test statistics is calculated using unequal variance case as:
to =
x¯1 − x¯2 − Δ0 s12 n1
+
s 22 n2
(8)
where, x¯1 and x¯2 represent the mean temperature values for compared two materials respectively, Δo is hypothesized mean difference which is equal to zero, s1 and s2 are variances of material one and material two that are compared, while n1 and n2 represent number temperature records in compared materials one and two. If the calculated value of to from Eqs. (8), (7) is less than t-critical value taken from table (to > tcr or p-value < α), it indicates that there is significant difference in performance between two compared energy storage materials, otherwise significant differences do not exist. 3. Results and discussion
3.2. Summer thermal performances 3.1. Boundary conditions The experimental results of temperature variation between PCM and water compared to the air filled glazing system over 48 h for summer season are recoded and shown in Fig. 3. Fig. 3(c) shows temperature histories at interior glazing surface (in the chamber), which are represented with the different colors in the diagram. For PCM filled glazing system, the recorded temperature is represented with red color in the diagram, blue color represents recorded temperature at interior surface of a glazing system filled with water, and for the comparisons the green one represents the temperature record at interior surface of a conventional glazing system filled with air. It is observed that during cloudy sky between 30 and 33 h in Fig. 5, the temperatures on the interior glazing surface for air and PCM is more sensible than with water sample. The temperature trajectories are steeper than with water sample indicating that the cooling process of the pane is faster. Each of these three temperature records have
The boundary conditions of exterior air temperature and solar irradiance on the vertical plane (VP) for all three tested samples over 48 h, in summer and winter seasons are recorded and shown in Fig. 2. Fig. 2(a) and (b) represent boundary conditions for summer case of exterior air temperature and solar irradiance on vertical glazed surface during the experiment, while Fig. 2(c) and (d) represent boundary conditions in winter season for exterior air temperature and solar irradiance on vertical glazed surface. The experiment was conducted in late of June and December of 2017 in Sarajevo - Bosnia. The sky was clear without wind presence. In summer season, presence of cloud afternoon (Fig. 2(b)) was noticed in the second day of the experiment, which was recorded as noise in the diagram. The air temperature in was between + 7 °C and + 34 °C over the cycles, while the irradiance on vertical plane reached 530 W/ 425
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Fig. 2. Boundary conditions: exterior air temperature (a) summer and (c) winter; irradiance on VP (b) summer, (d) winter.
temperature variation and reduced radiation caused by clouds. The peak temperatures for each material are close even thought higher the PCM has higher thermal conductivity than water (see Table 1). Compared to the PCM and water glazing systems, air is considered as a good insulator due to its low heat conductivity but in presence of solar radiation, the traditional air glazing system has higher temperature variation and consequently higher impact to the heating /cooling load. In case of PCM and water glazed heat exchanger, overheating is reduced by accumulation of heat energy in these materials. The storage system can be optimized for the certain climate zone by adopting appropriate cavity thickness (Durakovic & Torlak, 2017a). The peak in traditional air glazing system has higher surface temperature for about 4 °C that PCM glazing system and 6 °C than water glazing system. PCM and water glazing systems have approximately three and one hour delay of temperature during the melting / heating process. The significance of these peak temperature differences are tested in using F-test and the results are shown in Table 4. The p-value is significant for each comparison indicating that damping properties of the water and PCM sample are significantly better compared to the air sample. Samples with lower variance in Table 4 indicate that temperature they have better damping properties. Comparative performances of these materials resulted from ANOVA at 95% confidence interval is shown in Table 5, where indicate that
Fig. 3. Histories of surface temperatures for water, PCM and air glazing system (summer case). Table 3 Correlation between temperature histories.
Water Air PCM
Water
Air
PCM
1 0.931 0.964
1 0.911
1
similar trends over the period; especially water and air filled glazing systems. Due to phase change process in PCM filled glazing system, the temperature history slope during phase transition period is decreased qualifying this system as most promising. The degrees of similarities are recorded in the correlation table, Table 3. As it was mentioned, water and the air glazing system have a similar trend with a correlation coefficient of 0.93 while PCM and the air have slightly decreased correlation coefficient of 0.911 due to phase transition period. It is observed that there are some daily variations in the temperature histories caused by variable weather conditions such as air
Table 4 Comparisons of temperature damping properties for summer case.
Mean Variance Observations df F P(F < =f) one-tail Fcr one-tail
426
Air
PCM
Air
H 2O
pcm
H 2O
26.3 225.9 41103 41102 1.38 7.2E-242 1.02
24.6 162.7 41103 41102
26.2 225.9 41103 41102 1.43 1.6E-293 1.02
26.7 157.3 41103 41102
24.6 162.7 41103 41102 1.03 0.0003 1.02
26.7 157.3 41103 41102
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Table 5 Single factor ANOVA results for summer case. ANOVA Source of Variation
SS
df
MS
Fo
P-value
F crit
Between materials Within material
100465.8 22439749
2 123306
50232.91 181.9842
276.02
2.5E-120
2.99
Total
22540215
123308
Table 6 Two-Sample t-test results for summer case. Material 1
Material 2
Mean (oC)
Variance
Significant (p‒value)
Fig. 5. Temperature histories for air, water and PCM glazing system (winter case).
Air
Water PCM Air Water
26.7 24.6 26.3 26.7
157.3 162.7 225.9 157.3
1.1E-05* 0* 0* 0*
3.3. Winter thermal performances
PCM
The experimental results of temperature variation at interior glass surface between PCM and air filled glazing system over 48 h for winter season are recoded and shown in Fig. 5. Since these record were done in winter season water sample was out of consideration due to its freezing point at 0 °C. Referring to Fig. 5, red and blue colors in the diagram represents temperature histories of PCM and air filled glazing system respectively. Conventional air filled sample was more sensible to solar irradiance variation than PCM sample. PCM sample due to energy stored as latent heat during melting are capable of keeping the temperature of the glazing above 20 °C and contributing to space heating over 10 h period. After the temperature falls below 20 °C the sample start to take heat from the space but temperature difference between room temperature and the PCM system is more favorable than with the air sample. The results of thermal performance analysis of these two materials are given in Table 7. From the data analysis results in Table 7, it is observed that the variance and the average temperature differ significantly, which is explained by F and t-tests. For F-test observed value is 1.8 which is greater than its Fcr = 1.14. It tells that the variance of the air sample is significantly higher the variance of PCM sample, which means that temperature damping properties of the PCM are significantly better in the winter season as well. Based on t-test results, observed t-value is 5.29, which is higher than its critical value tcr = 1.96, thus it can be claimed that the difference between average temperatures of the air and the PCM is significant in winter season as well. In another words, the PCM has significantly higher average temperature (10.7 °C) compared to the air sample (5 °C), which means it had better thermal properties in terms of energy dissipation in winter. With the aim of assessing experimental error impact on the results for both seasons, another analysis was done including temperature
* significant at p < 0.0167.
there is at least one significant difference. From ANOVA table, F and p‒values indicate that performances of at least one material differ significantly. Also, from F-test it is observed that there is a significant difference among temperature history variances for each material. Since, significant difference is observed for at least one material, post hoc t-test is applied to indicate which mean temperature differs significantly and the results are shown in Table 6 and Fig. 4. Referring to Fig. 4 and Table 6, it is observed that p ‒value is much lower than significance level α, thus there is strong evidence that significant difference among materials applied in a glazing system exists. The water sample variance value of 157.3 is the lowest one, which indicates a good temperature damping properties of this glazing system by ranking this sample as number one. A high average temperature value of 26.7 °C makes water sample as unfavorable. The PCM sample variance value of 162.7 is slightly higher than the one observed with water sample, which place this sample at number two in the rank of temperature dumping. This variance value indicates a good temperature damping properties as well, which are significantly better than the traditional air sample. Observed average temperature of the PCM sample was 26.3 °C, which is slightly lower than the one observed with the air sample. Since PCM sample has the property to absorb energy during the phase change (latent heat), delayed heat gain/loss was observed which is beneficial for temperature dumping time. Combining these three properties (variance, average temperature and temperature dumping time) of the PCM glazed system; it was observed that PCM sample had good performances for each of them and as result the temperature stayed stable for longer period compared to the other samples.
Fig. 4. Mean temperature and variance - a graphical representation. 427
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sensor accuracy range. The results showed that there is no change in average temperature values, thus the significant differences concluded above were confirmed. Temperature amplitudes were negligibly changed but significant results were confirmed again, which means that experimental error does not affect the measurement.
Table 7 PCM vs. air sample comparative F and t-test results. Materials
Mean (oC)
Variance
PCM Air
10.7 5.0
237.1 428.7
F-test t-test
Observed 1.80 5.29
Critical 1.14 1.96
Significance (p‒value) 8.6E-13* 7.1E-08*
3.4. Heat exchange between glazing system and indoor air Heat gain/loss from the system to the room is function of the surface temperature. Radiation component and convection components of heat
* significant at p < 0.01.
Fig. 6. Sensitivity analysis of heat transfer between glazing system and room for = 4; 5 and 6 W/m2K ; (S - summer; W - winter). 428
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Fig. 7. Comparison of total heat exchange for each material per season (S - summer; W - winter). Table 8 Result summary of comparative analysis. Winter
PCM tot
Air tot
Sign.
Mean Variance
−69.23 16813.59
−132.69 33323.75
Yes* Yes*
Summer Mean Variance
PCM 4.643024 17,119.91
Air 23.32046 24184.98
Sign. **
Yes Yes**
H2O
Air
25.86258 16,589.55
23.32046 24184.98
Sign. **
Yes Yes**
H2O
PCM
Sign.
25.86258 16,589.55
4.643024 17,119.91
Yes** Yes**
* Significant at p = 0.05. ** Significant at p = 0.017.
procedure explained earlier for thermal performances analysis was done and it was observed that significant differences exist in heat exchange between the room and glazed system following the conclusions earlier drawn with temperature analysis. This is expected because the heat transfer is function of the surface temperature. The result summary is shown in Bases on Table 8, each comparison has significant p-value for variances and mean heat exchange indicate significant differences in the thermal behavior, which is confirmed with ANOVA and post hoc t-tests, while F-tests confirmed significant difference between amplitudes of the heat transfer.
gain/loss to the room for each material is shown in Fig. 6. Sensitivity analysis for convection component of the heat exchange is performed using three values of convective heat exchange coefficient, which are adopted in the vicinity of the recommended values for test cells (Goia & Serra, 2018). The data in Figs. 6 and 7 are obtained by simulation. Component of 4 4 − Troom ), radiative heat transfer rate is obtained using QR = ε (Tsurf where ε is emissivity of the glass surface. Convective component is obtained using Qconv = α (Tsurf − Troom) , where α is convective heat transfer coefficient. Values for emissivity were taken from standard literature (Troom = 297.15 K for summer case; and Troom = 293.15 K for winter case; ε = 0.84) (ASHRAE, 2009), while convective heat transfer coefficient at interior glazing surface for test cell settings are recommended α = 4 to 5 W/m2K for summer case; α = 4.5 W/m2K for winter case (Goia & Serra, 2018), but for this study adopted values for the purpose of sensitivity analysis are 4; 5; and 6 W/m2K . Generally, increase of the convective heat transfer coefficient value from 4 to 6 W/m2K caused a slight increase in heat exchange between the glazed surface and interior environment. The convective heat exchange is less active during the night in summer for each value of the coefficient, which is expected due to lower temperature difference. In winter, the impact of convective heat transfer coefficient on the heat exchange is notable over the night due to a higher temperature difference between glazed surface and the room. Total heat exchange for each material was calculated as sum of these two Qtot = QR + Qconv and displayed in Fig. 7. For winter season it is obvious the significant differences in the heat exchange exists between air-based sample and PCM-based sample. The PCM saves more heat by delaying the rapid heat loss from the room for about 4.5 h. In summer season the case is similar, the PCM sample delays heat gain and rapid heat loss to/from the room for about 5 h in total, while the water-based sample shown potential in delaying of the heat gain/loss as well. Overall thermal performance these three materials were analyzed based on α = 5 W/m2K . Applying the same
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