Impact of translucent water-based acrylic paint on the thermal performance of a low cost house.

Abstract

Insulation materials are selected based on their R-value, which is a measure of the thermal resistance of a material. Therefore, the higher the R-value of a material, the better its thermal insulation performance. There are two major groups of insulation materials: bulk and reflective insulation (or combine bulk and reflective). Bulk insulation is design to resist heat transfer due to conduction and convection. Reflective insulation resists radiant heat flow due to its high reflectivity and low emissivity. Insulation materials are not restricted to these materials only. Other low thermal conductive materials can be used as long as the primary aim of thermal insulation, which is increasing thermal resistance, is achieved. Hence, the aim of the project is to investigate the insulation ability of Translucent Water-based Acrylic Paint (TWAP) on the thermal performance of Low Cost Housing (LCH). To achieve the aim of the study, the inner surfaces of the external walls of LCH was coated with TWAP. Before the inner surfaces of the external walls were coated, the following techniques were used to characterised the paint; Scanning Electron Microscopy/ Energy Dispersive X-ray spectroscopy (SEM/EDX), Fourier Transform Infra-Red (FTIR) and IR thermography. SEM/EDX was adapted to view the surface morphology and to detect the elemental composition responsible for the thermal resistance of the TWAP. FTIR spectroscopy was used to determine the functional group and organic molecular composition of the paint. The heat resistance of TWAP was analyzed using IR thermography technique. A low cost house located in the Golf Course settlement in Alice, Eastern Cape, South Africa under the Nkonkobe Municipality Eastern Cape was used as a case study in this research. The house is facing geographical N16°E, It comprises a bedroom, toilet and an open plan living room and kitchen. The house has a floor dimension of 7.20 m x 5.70 m, giving an approximate area of 41 m2. The roof is made of galvanized corrugated iron sheets with no ceiling or any form of roof insulation. The walls of the buildings are made of the M6 (0.39 m x 0.19 m x 0.14 m) hollow concrete blocks, with no plaster or insulation. The following meteorological parameters were measured: temperature, relative humidity, solar irradiance, wind speed and wind direction. Eleven type-K thermocouples were used to measure the indoor temperature, inner and outer surfaces temperature of the building walls. Two sets of HMP50 humidity sensors were used to measure the indoor and outdoor relative humidity as well as the ambient temperature. The indoor temperature and relative humidity were measured at a height of 1.80 m so as to have good indoor parameter variation patterns that are not influenced by the roof temperature. The outdoor relative humidity sensor together with a 03001 wind sentry anemometer/vane and Li-Cor pyranometer were installed at a height of 0.44 m above the roof of the building. Wind speed and direction were measured by the 03001 wind sentry anemometer/vane, while solar radiation was measured by the Li-Cor pyranometer. The entire set of sensors was connected to a CR1000 data logger from which data are stored and retrieved following a setup program. The SEM image shows that TWAP is transparent in its dry state. EDX spectrum reveals the presence of Al in the paint, which is present as Al2O3. Due to the refractive index (1.73) of Al2O3, it is used as IR reflective pigment in reflective paints. Other elements like Si were also identified as SiO2 that contributes to the thermal resistance of the paint. The functional groups that made up the different molecular bonding of the paint were clearly shown by the FTIR spectrum. These include O–H, CH2, Si–H, C═C and others. From the IR thermography, average decrement factor of 0.54 and 1.04 were found for the coated and uncoated, respectively. This implies that the coated surface has more heat reduction than the uncoated surface. The indoor temperature was observed to be influenced by the temperature of the building envelope in both summer and winter seasons. In summer, it was observed that the indoor temperature variation closely follow the external wall temperature. On a typical summer hot day, the average maximum heating rate of the North, West and South walls were 5.93 W, 5,07 W and 2.24 W, respectively. It was found that the indoor temperature was mostly higher than the outdoor temperature. Also, that the indoor temperature was within the comfort zone for only 46% of the time. The indoor relative humidity was within the comfort zone throughout the day. A time lag of 2.5 hours was observed between the time the solar radiation and indoor temperature were at their maximum value. In the winter season, it was found that the indoor temperature is influenced more by the middle and external walls temperature. On a typical cold winter day, the average cooling rate for the North, West and South walls were found to be 0.89 W, 8.27 W and 0.30 W, respectively. The indoor and outdoor temperatures were seen to be completely below the comfort zone. On the other hand, the indoor relative humidity was found at the upper region of the comfort zone, ranging from 55% to 65%. It was found that the wind speed experienced in winter was 12% higher than the maximum wind speed in the summer season. After coating, the thermal performance of the building showed significant improvement. The amount of cooling and heating degree hour required by the inner space of the house to maintain thermal comfort, decreased by an average of 50% and 41%, respectively, in both winter and summer periods. Finally, the house was found to save an average of 37% of cooling and cooling energy demand after coating. The model developed shows that all components of the building envelope contributed positively to the indoor temperature, in the summer. Indoor, outdoor relative humidity and winter speed contribute negatively to the indoor temperature. In the winter, it was observed that the floor and South walls contributed negatively to the indoor temperature, as well as the wind speed and indoor relative humidity. In both seasons, the measured and predicted indoor temperatures showed a best fit with approximately 99% of the response data set perfectly on the predicted temperature. This proves that the predictors are the parameter that can serve as the basis for indoor temperature.