Analytic testing

Analytic tests simply refer to heat balance formulation and solutions of problems but are of limited applicability due to their ability to calculate only static parameters as shown in studies by Durmayaz, Kadıoglu et al. (2000); Li and Lam (2000); Lam et al. (2004);

Kanda, Kawai et al.(2005); and Xiao et al. (2009).

Latent heat transfer problems for PCMs are complex and challenging for analytical calculations. Analytic calculations require a simplification of complex energy flows in buildings. However, building energy flows are dynamic and complex. These

assumptions and simplications, in addition to the non-dynamic nature of analytic calculations of complex and dynamic building energy flows limit them from being used for detailed PCM performance examinations.

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Neural network (NN) models or neuromorphic systems are examples or analytic building energy calculating techniques based on operational principles similar to those of the human brain. A neural network can be considered, in general, as a set of linked units able to connect an input phenomenon (Caudana et al., 1995). In their work, Neto and Fiorelli (2008) model a simple problem based on artificial neural network as an auditing and predicting tool in order to forecast building energy consumption. The results for the neural networks showed a fair agreement between energy consumption forecasts and actual values, with an average error of about 10% when different networks for working days and weekends are modelled.

Degree-days, another analytic method calculated through regression techniques has been used to model predictions for energy performance indicators (Chung and Hui, 2009) and thermal performance of PCM (Zalba et al., 2004).

Of particular importance to this research, Darkwa and O’Callaghan (2006) gave a formulation for calculating effective heat capacity, Ceff, of PCMs as shown in Equation 4-1 as:

^{( )} Equation 4-1

Where Cs is specific heat capacity, a is the total amount of latent heat, T is the coefficient of solar radiation, T_m is transition temperature and b is the width of phase change zone. The use of such an equation is further illustrated in Chapter 7.

In a study, Xiao et al. (2009) analytically calculated the optimal transition temperature and the total amount of latent heat capacity. The equation of the optimal transition temperature, Tm of interior PCM in a lightweight passive solar room is obtained as Equation 4-2:

Equation 4-2

Where T_a is the average room temperature, Q_r is the transmitted solar radiation on the interior surfaces (W), Qr,in is the radiation heat transfer rate from indoor heat sources (W), h_in is the heat transfer coefficient of interior surface (W m⁻²

°C⁻¹), P are the duration (s) and Ain is the area of interior PCM panel (m²)

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Experimentation is usually conducted for problems that are difficult to analytically solve or simulate. There are two types:

 Laboratory testing

 Field testing

4.2.2.1 Laboratory testing

Laboratory techniques for examining PCM performance are classified broadly into its properties and applications. Laboratory techniques used for calculating the thermo-physical microstructures of PCMs are usually by scanning electron microscope SEM.

Latent heat of fusion and melting temperature of PCMs are measured by differential thermal analysis (DTA), and differential scanning calorimeter (DSC) (Atul Sharma, 2009). In DSC and DTA techniques, sample and reference materials are heated at constant rate. The temperature difference between them is proportional to the difference in heat flow between the two materials and the record is the DSC curve. The

recommended reference material is alumina (Al₂O₃). Latent heat of fusion is calculated using the area under the peak and melting temperature is estimated by the tangent at the point of greatest slope on the face portion of the peak.

Atul Sharma (2009) argues that DSC fails to provide correct information; due to a temperature gradient inside the PCM and depends on the heating/cooling rate and sample mass; or meaningful information on super-cooling. Latent heat and melting point are better determined by thermal analysis.

Castello et al. (2008) investigate different measurement procedures of DSC to determine enthalpy temperature relationship of PCM and claims to increase the DSC performance to a satisfactory level.

Other tests performed on the PCM include the thermal stability by a melt–freeze cycle test and thermal conductivity with ready-made measuring apparatus.

A thermal performance test was conducted on PCM plates (Li et al., 2009). They were put into the constant temperature drying oven for the melting process then immediately subjected to the solidification process in the refrigerator performed consecutively up to 100 thermal cycling. The surface temperature variations of the samples during these processes were automatically recorded to a computer via a data-logger. This method is adequate for assessing just the thermal performance of the plate for preliminary studies on PCM products rather than buildings.

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Alawadhi (2008) presents the thermal analysis of a building brick containing PCM in a laboratory test. The model consists of bricks with cylindrical holes filled with PCM.

The thermal effectiveness of the proposed brick-PCM system is evaluated by comparing the heat flux at the indoor surface to a wall without the PCM during typical working hours. A paramedic study is conducted to assess the effect of different design

parameters, such as the PCM’s quantity, type, and location in the brick. Four different cases are investigated, bricks with one, two, and three PCM cylinders, as well as, a brick without PCM. Three type of paraffin PCMs are examined: octadecane, n-eicosane, and P116. PCM transition temperature is within the operating temperature of the system- a statement that fails to show optimization of the transition temperature.

The P116 and noctadecane are ineffective in reducing the heat flux to the indoor space because transition temperature is too low and too high respectively. However, when n-eicosane is introduced, the rate of change of the heat flux is substantially reduced during the period from 10 a.m. to 5 p.m., with a maximum heat flux reduction of 24.2%

because its transition temperature is optimum. Having a minimum quantity of the PCM is desirable to maintain the strength of the brick. Results indicate that with only one cylinder the maximum heat flux is reduced by about 11.5%, and 17.9% with two cylinders. When three cylinders are used, the reduction reaches 24.2%. The results indicates that having PCMs in the building fabric increases the heat capacity of the fabric and reduces heat gain by absorbing the heat before it reaches the interior.

The limitations of laboratory testing is evident in the study. These include over-simplifying such processes such as solar incidence and heat flux through construction materials.

4.2.2.2 Field testing

Field experiments present the most realistic results because they have been shown to work in practice if successful report Cabeza et al. (2007).

PCM performance has been evaluated in the field by Kuznik et al. (2011). The

investigation studies building component, method of incorporation, human comfort, and PCM thermo-physical properties. The PCM material used is Energain by the Dupont de Nemours Society (2012). It is composed of 60% of micro-encapsulated paraffin, which has a melting temperature of about 22 °C. The final form of the PCM material is a flexible sheet with a density of 1019 kg/m³. In order to assess the potential of PCM wallboards, two offices in a renovated office building are monitored over a year. One has PCM wallboards in the lateral walls and in the ceiling, while the other room,

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identical to the first one, was not equipped. The first set of experimental data analysed was for a week-end (17^th and 18^th) in November in an effort to limit the effects of building occupants to the two occupants of the offices behaving in a similar manner.

The maximum temperature of the room with PCM is lower than the maximum temperature of the room without PCM of about 2.2 °C conforming to earlier works reviewed. The week-end of March 28th/29th had temperature of the rooms rise to about 40°C. The PCM was completely in the liquid phase and the two rooms had very close air temperature indicating no latent storage effect in the wallboard. For the period between February and December, the difference between the room with PCM and the room without PCM is about 98 h for the number of hours for which the globe

temperature is above 29 °C. In conclusion, the thermal comfort is enhanced due to both the air temperature and the walls surface temperature. This particular effect is efficient if the building before renovation is of low inertia and if the temperature variations are around the phase change temperature of the PCM.