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Based on the arguments presented and discussed in the introduction and literature review, it clearly appears that, in recent years, there has been a proliferation of air-conditioning in both residential and commercial buildings, and, due the growing disposable income in several densely populated developing countries such as India, the energy demand for space cooling is dramatically increasing. Moreover, in a warming climate and with a building stock that is becoming more energy efficient for the cold season, the need for appropriate cooling systems is likely to become essential to avoid overheating also in cooler north-European countries. However, mechanical systems, such as built-in air conditioning, consume a lot of energy because they cool down quite evenly the entire space, rather than the occupants.

Several field studies, mainly in extra-European countries, support the idea that air movement in warm environments improves thermal comfort, rather than causing the so-called draught sensation. This idea would lead to a lower use of air-conditioners, but also to transient and asymmetrical environments. Recent research claims that these conditions generate more pleasant environments, but the extent to which this will happen cannot be easily predicted by using the traditional PMV model in the design stages.

The adaptive thermal comfort model can be used to evaluate the acceptability of certain temperature conditions when adaptation can take place such as in residential buildings. However, such a model does not allow for a direct comparison between the effect of different cooling design solutions, but simply shows that a certain tempera- ture range is acceptable in relationship to the outdoor temperature.

Designers need to be able to accurately predict the performance of different so- lutions to use them, and cannot rely on a generic possibility of adaptation. If from an energy point of view a solution is preferable, then designers should make use of this solution the easiest choice for the occupants. But to do so, a robust method to evaluate its performance is required.

CFD alone has been used to predict thermal comfort for years, and recently manual coupling with human thermal regulation models has been done. However, almost all studies focused on steady state conditions and the manual coupling was quite complicated. Moreover, there is almost no research which used data measured in actual residential buildings to evaluate CFD predictions.

In the literature it has been found that there is only one existing fully coupled model, which is based on the IESD - Fiala model (Fiala et al., 1999, 2001) and ANSYS CFX (2015). The coupled model can be used in a variety of transient and asymmetrical conditions, but it has never been tested in such a real environment. Therefore further research is needed to understand the applicability of this coupled model in order to produce reliable results, and hence to be able to promote low energy space cooling solutions such as increased air movement while not jeopardising the oc- cupants’ thermal comfort. Challenges include the identification of realistic scenarios, the collection of reliable data in real buildings or in realistic simplified experimental facilities, and the need to balance model quality results and computational power required. However, such a model will be widely usable not only in residential and non-residential buildings, but also in several other non-uniform environments such as air plane cabins and train coaches.

INITIAL TESTING OF THE

COUPLED SYSTEM:

ENVIRONMENTAL CHAMBER

STUDY

3.1

Overview of the experimental chapters

This chapter is the first of four experimental chapters. In general, their aim is to describe in detail the methods chosen to conduct this research and the reasons behind each choice, and to clearly illustrate the results of the different parts of this research highlighting the most relevant ones.

In this research, computer simulations and laboratory-based experiments were used extensively to fully address the aim and objectives of this project, and therefore to answer the research question. Moreover, this research benefited from the close link to a wider international project which included field work in residential buildings in the UK and in India. Although this field study was not designed as part of the PhD research, some of the data collected and the information obtained during this field

study were used for this PhD research.

In this research, CFD coupled in real time with a human thermal physiology and comfort model, the IESD-Fiala model (Fiala et al., 1999, 2001), was used to investigate thermal comfort in non-uniform thermal environments. The most relevant features of this coupled system are summarised in the following section 3.1.1.

In this study, this coupled system was initially applied to environmental chamber studies, and then to model more complex, but also more relevant, actual domestic buildings. Laboratory-based experiments were used to provide high-quality data to validate the CFD models. DTM was then used to estimate the likely energy saving that can be achieved in residential buildings by using air movement to increase the cooling set-point temperature without jeopardizing occupants’ thermal comfort in such non-uniform conditions.

The structure of these four experimental chapters is as follows. Chapter 3 illustrates the first application of the coupled system, which comprises measurements and computer modelling. Chapter 4 describes the work done to create and validate a detailed CFD model of a typical Indian ceiling fan, whose crucial importance will be introduced in section 4.1 in the same chapter. This section explains the connection with the wider international project, what real buildings were considered in this PhD research and why, within this sample, certain scenarios were chosen to be studied using the coupled system. Chepter 5 is about the application of the coupled system to real domestic buildings, which brings together the work undertaken in the previous experimental chapters. Lastly, Chapter 6 illustrates how DTM was used in this project for estimating energy savings due to air movement in mixed mode buildings in India.

3.1.1

CFD and IESD-Fiala coupled model: key features

The main characteristics of the IESD-Fiala model have been presented and discussed in section 2.3.3, while the whole coupled system has also been previously described

in section 2.3.4. However, at this point, it is useful to highlight the most relevant features of the coupled system in relation to this research. Furthermore, it is worth noting that, in the context of this research, the expressions “coupled model” and “coupled system” mean the same thing and therefore used interchangeably.

Firstly, the coupling was done using ANSYS CFX version 14.5.7 (Cropper et al., 2008, 2009, 2010; ANSYS CFX, 2015) and therefore this version of this commercial CFD program had to be used for this research. Secondly, in CFD, a generic occupant is represented by a virtual manikin. To couple the two components of the system, the surface of this virtual manikin must be divided into 59 parts, whose names must match those used by the IESD-Fiala model. Thirdly, the coupling between CFD and ISED- Fiala model currently works only with one virtual manikin per CFD model. Thus, other occupants may be included in the CFD model, but modelled only as passive uncoupled elements (for instance, simple boxes with a fixed surface temperature).

The coupling required transient CFD simulations to reach a converged solution due to the truly transient behaviour of the modelled phenomenon (Cropper et al., 2008, 2009, 2010). At each time step of the CFD simulation, for each of the 59 surface parts, body surface temperatures, near-wall air temperatures, convective heat transfer, coefficients, convective heat flux, radiative heat flux, and near-wall relative humidity are sent from the CFD to the IESD-Fiala model. After IESD-Fiala model has reached a converged solution, temperatures, moisture mass fractions, and emissivity values are sent back to the CFD model. The total duration of this process depends on the length of the period simulated in CFD. For instance, if the time-step in CFD was 10 s and the simulated period 120 s, then the exchange process would end when total simulated time would be equal to 120 s.

Lastly, it is essential to highlight that the coupled system generates two metrics that are related to thermal comfort, namely the Dynamic Thermal Sensation (DTS) and the PPD. The former is a thermal sensation index, and it uses the 7-point ASHRAE scale (from 3 to -3: hot, warm, sprightly warm, neutral, slightly cool,

cool, cold). The latter predicts the percentage of occupants that will be dissatisfied with the thermal conditions. The minimum level of dissatisfaction corresponds to a neutral thermal sensation (the mathematical relationship DTS-PPD is the same as PMV-PPD). In other words, this means that the number of people that are satisfied with their surrounding thermal conditions is maximum when their thermal sensation is zero (that is neutral). Based on these consideration, although the DTS is a thermal sensation and not a thermal comfort metric, it is possible to use it as a means to evaluate thermal comfort. The closer the DTS is to zero, the lower the number of dissatisfied people is.

3.2

Initial testing of the coupled system: environ-