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ScienceDirect Energy Procedia 78 (2015) 1901 – 1906

6th International Building Physics Conference, IBPC 2015

Validation of Computational Fluid Dynamics (CFD) Model of a Building Integrated Photovoltaic/Thermal (BIPV/T) System D. Roelevelda,*, G. Hailua, A.S. Funga, D. Naylora, T. Yangb, A.K. Athienitisb b

a Department of Mechanical & Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3 Department of Building, Civil and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec, Canada H3G 1M8

Abstract A building integrated photovoltaic/thermal (BIPV/T) system was studied using computational fluid dynamics (CFD). The BIPV/T system was composed of solar photovoltaic (PV) panels mounted in front of a wall/roof with an air channel in between. A numerical model was developed consisting of a PV layer, an air channel, and an insulation and plywood layer. Experiments were conducted by the Solar Simulator Laboratory at Concordia University using three different orientations of 0°, 45° and 90° at two different flow rates. A comparison of the temperatures profiles between the numerical predictions and the experimental data is presented. Overall, the numerical results were in good agreement with the experimental data. 2015The TheAuthors. Authors. Published Elsevier © 2015 © Published by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL. Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL Keywords: BIPV/T; Numerical Modelling; Computational Fluid Dynamics; Mixed Convection;

1. Introduction Computational fluid dynamics (CFD) was used to model building integrated photovoltaic/thermal (BIPV/T) systems. The BIPV/T system is composed of solar photovoltaic panels mounted in front of the building wall/roof with an air gap in between creating a channel. Air was used as a coolant in this channel in order to reduce the temperature of the PV panels and increase their efficiency [1]. This heated air can then be used in a heat pump or for

* Corresponding author. Tel.: +1-416-979-5000; fax: +1-416-979-5265. E-mail address: [email protected]

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the CENTRO CONGRESSI INTERNAZIONALE SRL doi:10.1016/j.egypro.2015.11.359

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thermal storage. The purpose of this study was to develop a numerical model and validate it against the experimental data obtained from the Solar Simulator Laboratory at Concordia University. There have been numerous studies on BIPV/T systems [2-6]. Most of these studies have involved conducting experiments to analyze the performance of the BIPV/T system and then develop a simplified mathematical model. Candanedo et al. [7] conducted an experimental study to develop heat transfer correlations for the top and bottom surface inside a BIPV/T system with forced convection. The correlations were developed for a BIPV/T system at a tilt angle of between 30° and 45° and a Reynolds number between approximately 250 and 7000. They found that the heat transfer of the top surface was higher than predicted by existing forced convection correlations. Liao et al. [8] conducted a numerical and experimental study of a BIPV/T system. A two-dimensional model was developed using the realizable k-İ turbulence model. Utilizing experimental data, the measured temperatures of the PV and insulation were used as the boundary conditions in the numerical model. The CFD predictions were in good agreement with the experimental velocity profiles that were obtained using particle image velocimetry. The convective coefficients were higher than expected due to leading edge effects and the turbulent nature of the flow. Convective heat transfer coefficient correlations were developed. Naewngerndee et al. [9] conducted a CFD study to investigate the effects of flow rate and PV panel configurations on BIPV/T systems. It was shown that a configuration of 4 × 4 was better than 8 × 2, and 3 × 4 was better than 4 × 3 or 6 × 2. They also determined that the optimized flow rate for a 4 × 4 configuration was approximately 1 – 3 L/min. Getu et al. [10] conducted a CFD study of an air based BIPV/T system using two amorphous PV panels. A similar numerical model to the current study was developed to predict the temperatures of the PV panel, air channel, and insulation. Air velocities between 0.5 and 2.0 m/s were studied using a horizontal orientation. Experiments were conducted at the Solar Simulator Laboratory at Concordia University and this experimental data was used to validate the numerical model. A two-dimensional model was tested using both a k-İ and k-Ȧ turbulence model, where it was determined that the k-Ȧ turbulence model yielded the best agreement with the PV temperature and the k-İ turbulence model yielded the best agreement with the air channel and insulation temperatures. Also, at lower air velocities, the numerical predictions were less accurate due to the low Reynolds number. In this study, a CFD model was used to investigate the air flow conditions and thermal behavior inside a BIPV/T system. Experimental data has been obtained from the Solar Simulator Laboratory at Concordia University for various flow rates and orientations of the BIPV/T system using mono-crystalline PV panels with a transparent backing. This experimental data was used to validate the numerical model. Mixed convection was used inside the air channel, where the flow was highly turbulent. A comparison of the temperature profiles inside the BIPV/T system was conducted between the numerical results and the experimental data. This included results from two different mass flow rates of approximately 174 kg/h and 232 kg/h at three different orientations of 0°, 45° and 90°. The numerical solutions were used to present various data, such as the average temperatures and heat transfer coefficients of the BIPV/T system. 2. Numerical Model of BIPV/T A numerical model was developed of a BIPV/T system based on the experiments carried out in the Solar Simulator Laboratory at Concordia University [11]. The BIPV/T consisted of two PV panels mounted above a layer of insulation and plywood separated by an air channel. A schematic of the BIPV/T is shown in Figure 1(a). The BIPV/T was 2.039 m long by 0.529 m wide by approximately 87 mm thick. A schematic of the PV panel is shown in Figure 1(b). The PV panel consisted of 5 layers of various materials, which included tempered glass, monocrystalline silicon PV cells, two adhesive layers and the transparent backing material. The thickness of the PV panel, tPV, was 4 mm. The air channel thickness, b, was 45 mm. On the other side of the air channel, typical construction materials made up the wall. In the experiments, there were extruded polystyrene insulation and plywood, with thicknesses of tINS = 25.4 mm (1 in) and tply = 12.7 mm (0.5 in). Utilizing these dimensions, a numerical model was created in the commercial multi-physics finite element analysis software COMSOL Multiphysics [12]. The model was two-dimensional with temperature dependent air properties. The flow was turbulent inside the channel, so a k-Ȧ turbulence model was used. The boundary conditions on the outside of the PV panel included the measured solar radiation as a heat flux, surface to ambient radiation, and convective heat transfer due to the wind. The measured solar radiation from the experiments was qsolar = 1030 W/m2.

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a

b Fig. 1: (a) Schematic of the BIPV/T system and (b) schematic of the PV panel

The PV panel produced electricity, which reduced the total amount of heat transfer through the BIPV/T system. In order to account for this, volumetric heat absorption was applied to the mono-crystalline silicon PV cells layer. This was calculated from the measured amperage (I) and voltage (V) that the PV panel produced in each case. For the surface to ambient radiation, qrad, the glass had an emissivity of 0.89 and the temperature of the simulated sky, Tsky, from the solar simulator experiments was used. It varied for each experiment, but Tsky was approximately 14.3 °C. The convective heat transfer due to the wind, hw, was calculated from Duffie and Beckman [13]:

h w = 0.86 Re L0.5 Pr

1

3

kf L

(2)

where kf is the thermal conductivity of the air, Pr is the Prandtl number of the air, and ReL is the Reynolds number based on the wind speed. It should be noted that L refers to the hydraulic diameter of the PV panel (i.e. L = 4 × (plate area) / (plate circumference)). The air properties were evaluated at the ambient temperature of the outside air, T, and the wind speed was 2.2 m/s for the experiments. On the backside of the BIPV/T, a natural convective heat transfer coefficient was used (hnat = 2.8 W/(m2 K)). For the air channel, forced convection was used to pump outside air through the channel in order to cool the PV panel. The mass flow rate of the air, ী, and the inlet air temperature, Tinlet, were measured in the experiments and used as inputs for the air inlet in the numerical model. Because a turbulence model was used, the turbulence intensity and turbulence length scale needed to be defined. A high turbulence intensity (lT = 15%) was used in order to increase the accuracy of the results. The turbulence length scale was calculated to be 0.07 × hydraulic diameter of the air channel. At the outlet of the model, a pressure outlet and outflow were used. The direct integration surface to surface radiation model was also used between the back of the PV panel and the front of the insulation inside the air channel. The emissivity of the back of the PV layer was 0.89 and the emissivity of the insulation was 0.5. A volume force was also added to the air channel in the numerical model to account for buoyancy forces. A grid study was conducted to ensure that the numerical solution was grid independent. Three grid sizes of 68,500, 33,335 and 15,225 elements were used with the vertical orientation and ী = 232 kg/h. A Richardson extrapolation [14] was performed using the PV heat transfer coefficient (hPV) as the critical variable to be tested. The numerical uncertainty for the grid size of 68,500 elements was 1.5%. The results are shown in Table 1 and the difference between the coarse and fine grid was 1.6%. Therefore, the grid size of 68,500 elements was determined to be sufficient for this study. 3. Results and Discussion Experiments were carried out at the Solar Simulator Laboratory at Concordia University. These experiments used three different orientations of horizontal, 45°, and vertical and two different mass flow rates of approximately 174 kg/h and 232 kg/h inside the air channel. Thermocouples were used to measure the temperature profiles of the backside of the PV panel, the surface of the insulation layer inside the air channel, and the centerline of the air .

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D. Roeleveld et al. / Energy Procedia 78 (2015) 1901 – 1906 Table 1: Grid sensitivity study for ী = 232 kg/h at a vertical orientation. Heat Transfer Grid Size

Number of Elements

Coefficient of PV hPV (W/m2)

Fine

68,500

15.55

Medium

33,335

15.46

Coarse

12,225

15.31

channel. This experimental data was used to validate the numerical model. Figures 2-4 show a comparison between the numerical results and the experimental data for the temperature profiles of the a) backside of the PV panel, b) insulation surface, and c) the centerline of the air channel. The average air velocity in the flow direction inside the air channel was approximately 1.7 m/s for ী = 174 kg/h, 2.3 m/s for ী = 232 kg/h, and 2.4 m/s for ী = 247 kg/h. Overall the numerical predictions showed good agreement with the experiment data. Figure 2 shows both ী = 174 kg/h and ী = 232 kg/h at a horizontal orientation. In Figure 2, both cases show that the numerically predicted PV and insulation temperature profiles were higher than the experimental data. For the air channel centerline, the numerical results compared favorably with the experimental data. It should be noted that the temperature drops at the center of the PV panel in the experimental data. This was due to the packing factor of the PV cells between the two PV panels. There was a gap in the PV cells at the joint between the two PV panels that allows the solar radiation to transmit through the PV layer. Figure 3 shows a 45° orientation for ী = 174 kg/h and ী = 247 kg/h inside the air channel. In Figure 3(a), both the PV and air channel numerical solutions showed good agreement with the experimental data, but the numerical results were higher than the experimental data for the insulation surface temperature. At the higher flow rate, Figure 3(b) shows the numerically predicted PV and insulation temperature profiles were higher than the experimental data, but the air channel centerline showed good agreement. Figure 4 shows the vertical orientation. The temperature profile of the air channel centerline showed good agreement and the insulation was higher for both flow rates. In Figure 4(a) the numerically predicted PV temperature was lower and in Figure 4(b) the PV temperature was higher than the experimental data. Table 2 shows the results from the numerical model for all cases studied. It should be noted that due to the radiation exchange between the PV and insulation, both surfaces were heating the air in the channel. As expected, the PV temperatures were higher and the heat transfer coefficients were lower for the 174 kg/h cases compared to the higher flow rates. The PV temperature was approximately 2.5 °C cooler at the higher flow rate, which increased the PV panels’ electrical efficiency. When comparing a mass flow rate of 174 kg/h, the heat transfer coefficients of the PV and insulation were similar for all three orientations. For the vertical and horizontal orientations at a flow rate of 232 kg/h, the heat transfer coefficients were also similar. As expected, the 45° orientation had higher heat transfer coefficients because of the higher mass flow rate of 247 kg/h. 4. Conclusion In this study, a numerical model was developed to predict the temperature profiles inside a BIPV/T system. This model consisted of a PV panel, air channel, and insulation and plywood. The air channel utilized mixed convection and a k-Ȧ turbulence model for the air flow. Various boundary conditions were used that are typical of a BIPV/T system, such as a solar heat flux, radiation exchange with the ambient, and a convective heat flux due to wind on the PV layer. A natural convective heat flux was used on the backside of BIPV/T system. The numerical results were compared with experiments conducted by the Solar Simulator Laboratory at Concordia University. Multiple orientations of 0°, 45° and 90° were tested at two different mass flow rates of approximately 174 kg/h and 232 kg/h. The predicted temperature profiles inside the BIPV/T system were validated against the experimental data and showed good agreement. Various results, such as the average temperatures and heat transfer coefficients, were also presented.


a

b

Figure 2: Comparison of the temperature profiles between the numerical results and experimental data for a horizontal orientation and mass flow rates of (a) 174 kg/h and (b) 232 kg/h

a

b

Figure 3: Comparison of the temperature profiles between the numerical results and the experimental data for a 45° orientation and mass flow rates of (a) 174 kg/h and (b) 247 kg/h.

a

b

Figure 4: Comparison of the temperature profiles between the numerical results and the experimental data for a vertical orientation and a mass flow rate of (a) 174 kg/h and (b) 232 kg/h.

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D. Roeleveld et al. / Energy Procedia 78 (2015) 1901 – 1906 Table 2: Comparison of the numerical results for all cases. Orientation

Mass Flow Rate ী

Reynolds

Average PV

Heat Transfer

Heat Transfer

Number

Temperature

Coefficient of PV

Coefficient of Insulation

Re

TPV

hPV

hINS

°C

W/(m2 K)

W/(m2 K)

kg/h Horizontal

174

9115

47.7

12.92

4.57

Horizontal

232

12165

45.8

15.65

5.80

45°

174

9182

46.4

12.84

4.50

45°

247

13056

43.8

16.25

5.90

Vertical

174

9151

47.4

12.84

4.57

Vertical

232

12237

44.7

15.55

5.73

Acknowledgements This research was supported by Connect Canada through the Ontario Centres of Excellence (OCE), NSERC Engage through the Natural Sciences and Engineering Research Council of Canada (NSERC), and s2e Technologies Inc.

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