Experimental Evaluation of the Heat Balance of an Interactive Glass Wall in A Heating Season
Abstract
:1. Introduction
2. IGW Prototype Design and Test Method
2.1. Description of the Tested Prototype
2.2. Description of Test Stand
2.3. Description of the Method for the Evaluation of IGW Thermal Efficiency
- RIGW—total thermal resistance of IGW ((m2·K)/W),
- UIGW—heat transfer coefficient of IGW (W/(m2·K)),
- ΔT—temperature difference on the wall’s sides.
- A—total pane area (m2).
- Cg—ratio of the outer glazing visible area (not covered by the glazing bead) to the total glazing inner area IGW (Cg = 0.89),
- Sg—the sum of solar irradiance transferred through the outer glazing (Wh/m2).
3. Results and Discussion
3.1. Empirical Heat Transfer Coefficient UIGW
3.2. Determination of the Solar Heat Gain Utilisation Efficiency (ηSHGU)
- —solar irradiation transferred through the outer glazing in time interval (t1:t2).
3.3. Validation of IGW Heat Balance Calculation Mode
3.4. Prediction of IGW Heat Balance in a Heating Season Based on Climate Database
- A—surface of IGW (1 m2),
- UIGW—heat transfer coefficient (after Equation (7)),
- Ti—indoor air temperature (Ti = 20 °C)],
- Seh—hourly sum of solar irradiation recorded in front of the glazing,
- g—gain factor (g = 0.55 according to the manufacturer’s specifications of the glazing used).
4. Conclusions
- (1)
- The prediction of heat balance calculated for a heating season for a selected locality indicated the overbalance of heat gains over losses in all the months of the season when the IGW was oriented to the south, south-east and south-west. The western and eastern orientations in November and December resulted in the predominance of heat losses.
- (2)
- The tests indicated that, unlike conventional windows, apart from transparency, owing to the use of phase-change materials (PCM) in the IGW structure, it has the capacity of giving up heat gains even eight hours after the sunset.
- (3)
- The test results confirm the potential of the interactive designs, which apart from transparency, have the capacity to reduce the conventional energy demand and exert a favourable impact on the functionality of a building and occupants’ comfort. The use of cutting-edge technologies and their increasing availability for the shaping of the outer envelope of a building opens up new possibilities of construction engineering.
- (4)
- The validity of the method discussed in the paper indicated a difference in the heat balance calculated on the basis of the recorded heat flux density at the level of 15.53% of heat gains underestimation or its losses overestimation, which can be considered a result satisfactory with respect of calculations safety.
5. Patents
Funding
Acknowledgments
Conflicts of Interest
References
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Kind of Sensor | Type of Sensor | Accuracy |
---|---|---|
Temperature sensor | PT1000 | A class (<0.2 °C) |
Irradiaton sensor | DeltaOhm LP Pyra12 DeltaOhm LPPyra03 | <1 % (first class) <±0.2% (second class) |
Heat flow density | ALMEMO FQ A020 C | <6% of measured value |
Comet MS6D 16 Channel Data Acquisition Monitoring System | ||
DC | 4 to 20 mA | ±0.1% (±0.02 mA) |
DC | −10 V to +10 V | ±0.1% (±10 mV) |
Temperature | PT1000 | ±0.2 °C (−200 °C to +100 °C) |
Number of Day | |||
---|---|---|---|
1 | 3.409 | 20.70 | 0.165 |
2 | 3.685 | 20.67 | 0.178 |
3 | 3.425 | 20.12 | 0.170 |
4 | 3.375 | 20.16 | 0.167 |
5 | 3.242 | 20.02 | 0.162 |
Mean value | 0.168 | ||
Standard deviation | 0.006 |
Number of Day | |||
---|---|---|---|
1 | 5.08 | 20.14 | 0.252 |
2 | 5.05 | 20.45 | 0.247 |
3 | 5.38 | 20.23 | 0.266 |
Mean value | 0.255 | ||
Standard deviation | 0.01 |
Number of Day [n] | ηSHGU | ||
---|---|---|---|
10 | 131.61 | 608.1 | 21.64 |
11 | 276.60 | 1440.8 | 19.20 |
12 | 289.75 | 1398.1 | 20.73 |
13 | 249.51 | 1190.6 | 20.96 |
14 | 236.32 | 1143.3 | 20.67 |
15 | 104.56 | 438.6 | 23.84 |
Mean value | 21.17 | ||
Standard deviation | 1.53 |
QH−QS | ||||
---|---|---|---|---|
575.89 [Wh] | 3084.79 [Wh] | 2419.46 [Wh] | 665.33 [Wh] | 15.53% |
Month | Heat Balance [Wh/m2] | ||||
---|---|---|---|---|---|
E | SE | S | SW | W | |
October | −2442.03 | −3526.65 | −4226.97 | −3592.03 | −2492.84 |
November | 42.10 | −993.54 | −1691.16 | −1240.25 | −132.42 |
December | 835.30 | −237.27 | −841.21 | −390.31 | 727.05 |
January | 764.06 | −571.02 | −1160.47 | −421.21 | 870.04 |
February | −884.79 | −2124.54 | −2690.94 | −1839.41 | −692.03 |
March | −3260.19 | −4298.28 | −4679.78 | −3861.34 | −2921.9 |
April | −6536.78 | −7243.21 | −7272.98 | −6953.11 | −6259.6 |
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Szyszka, J. Experimental Evaluation of the Heat Balance of an Interactive Glass Wall in A Heating Season. Energies 2020, 13, 632. https://doi.org/10.3390/en13030632
Szyszka J. Experimental Evaluation of the Heat Balance of an Interactive Glass Wall in A Heating Season. Energies. 2020; 13(3):632. https://doi.org/10.3390/en13030632
Chicago/Turabian StyleSzyszka, Jerzy. 2020. "Experimental Evaluation of the Heat Balance of an Interactive Glass Wall in A Heating Season" Energies 13, no. 3: 632. https://doi.org/10.3390/en13030632
APA StyleSzyszka, J. (2020). Experimental Evaluation of the Heat Balance of an Interactive Glass Wall in A Heating Season. Energies, 13(3), 632. https://doi.org/10.3390/en13030632