1. Introduction
Approximately 40% of global energy is consumed in buildings [
1], and 50% of building energy is used to maintain indoor comfort by utilizing heating, ventilation, and air conditioning (HVAC) auxiliary systems [
2]. The depletion of energy resources and the risk of climate changes (i.e., global warming, melting glaciers, heavier rainstorms, more frequent drought. etc.) demand a sustainable development path based on renewable energies and energy efficiency. One of the most substantial issues is designing and constructing buildings that can provide inner comfort conditions by optimizing the use of natural resources. Consequently, the pollution usage of HVAC auxiliary systems can be reduced to a minimum.
Climate responsive or energy passive building designs can play a significant role in reducing the energy demand of buildings without compromising modern living standards. The so-called “bioclimatic approach to architecture” started with the research of Victor Olgyay (1963) [
3], which aims to achieve optimal comfort preferably using architectural elements and avoid complete dependence on mechanical systems. Although the term “bioclimatism” appeared as late as the mid-20th century, the bioclimatic approach in architecture can be traced back to the passive design principles applied in worldwide vernacular architecture. The lack of artificial means, such as HVAC, required past designers to build houses by completely utilizing the opportunities offered by the climates [
4]. These design principles or strategies were developed by “trial and error” and passed down for hundreds of years, leading to the diversity of architectural forms in response to the worldwide climates. In summary, the analysis of vernacular architecture, the knowledge of which has been accumulated for centuries, is fundamental to understanding the climatic behavior of a building in its environment and adapting this knowledge to current societal customs.
Before application to modern architecture, the effects and mechanisms of these strategies and techniques should be scientifically studied. The key steps of the research on vernacular architecture have gone through stages from empirical methods to analytical and computational modeling methods. Until the 1960s, these methods were summarized into scientific publications [
3,
5]. Scientific studies of the adaption of these methods to modern architecture started in 1977 [
6]. After the 1990s, numerical simulation was applied to validate and verify building performance with the support of advanced computer technology [
7].
The convective heat transfer on the building surface is of scientific and practical interest in the analysis of vernacular architecture. Especially in the field of architectural environmentalism, airflow has a considerable influence on heat loss and surface temperature at the external and internal building surfaces [
8]. Convective heat transfer of the external building surface transmits heat to the atmosphere, which is one of the main reasons for formation of urban heat island effect and energy losses in buildings [
9,
10]. The internal convection of buildings affects temperature distribution of indoor air, buoyancy-driven flow motion, and mean radiant temperature, affecting human comfort [
11,
12]. Normally, convective heat transfer at a building surface is modeled by the convective heat transfer coefficient (CHTC,
) [
8,
12,
13,
14,
15,
16,
17,
18]. The CHTC (
) relates the convective heat flux (q) to the difference between surface temperature
and environmental air temperature
.
The CHTC is important in assessing the energy performance of vernacular architecture and is used to calculate the convective heat losses. Substantial research has focused on the formulation of models to estimate the CHTC. Based on wind tunnel experiments on convective heat transfer on flat plates [
19,
20,
21] and bluff bodies [
22,
23,
24,
25], as well as field measurements for building facades [
13,
26,
27,
28,
29,
30], several correlations have been developed and widely applied in modeling, simulations, and calculations for building energy consumption. However, the CHTC at the facade of a building is complex and influenced by a wide range of parameters, including building geometry [
31], building surroundings [
32], discrete measurement position on the building façades [
17,
18], facade roughness [
33], wind speed, and wind direction [
34,
35]. All these experiments and field measurements somehow have their shortcomings in terms of limited physical similarity regarding the flow pattern, architectural shapes and boundary conditions. Therefore, these case-specific correlations have the limitation of applicability in the energy analysis of vernacular architecture.
Computational fluid dynamics (CFD) has recently become another option for predicting CHTC on building surfaces [
15,
16,
17,
18,
32,
35]. One of the main advantages of CFD lies in its ability to obtain high spatial resolution for a specific and complicated architectural shape. That is, CFD simulations can easily provide a sufficient amount of local CHTC distributions of each building surface prior to using empirical equations or field measurements. In addition, the flow and temperature fields are available. Previous studies mostly used steady Reynolds-averaged Navier–Stokes (RANS) to model the flow and temperature fields compared with unsteady RANS (URANS) or large-eddy simulations (LES). The RANS equations govern the transport of the averaged flow quantities, with the whole range of the scales of turbulence being modeled. The RANS model, therefore, greatly reduces the required computational effort and resources, and is widely adopted for practical engineering applications. In terms of CHTC prediction, previous studies normally focused on simple shapes (i.e., cubes) [
31]. However, in the simulation of vernacular architecture, the building geometry can be relatively complex, and the number of cases can be quite large. Therefore, an effective and relatively accurate methodology is required. That is, turning the problem into a balance between computational cost and accuracy is a concern of architects and engineers. Another factor that affects simulation effectiveness is the modeling of the near-wall region, which includes low-Reynolds number modeling (LRNM) and wall function approaches. For LRNM, a very high grid resolution with high computational cost is required near the wall. Wall functions are commonly used instead because of their low grid requirement. However, the accuracy of wall functions must be validated in terms of the convective heat transfer issue.
The main aim of the study presented in this paper is to seek an appropriate methodology for simulating the convective heat transfer in vernacular architecture considering computational cost and accuracy. First, a CFD validation study on the CHTC is performed based on wind-tunnel measurement of a wall-mounted cube immersed in a turbulent boundary layer. Three steady RANS models (namely, the standard k-ε (sk-ε), re-normalization group k-ε (RNG k-ε), and shear-stress transport k-ω (SST k-ω) models) and two boundary layer modeling approaches (wall functions and low-Reynolds number modeling) are compared and discussed. The details of the numerical simulation are described in
Section 2, and the simulation results are discussed in
Section 3. Next, a typical architecture form of vernacular architecture in Nala, Japan, namely, “machiya,” is simulated as an application example. The “machiya” is a typical case of Japanese vernacular architecture, which organizes effective cross ventilation to reduce indoor humidity and hotness. The convective heat transfer accounts for a large part of the heat losses in “machiya”, which is the main reason it is selected and simulated. The simulation results of the CHTC are compared with different empirical correlations, and the flow field verifies the ventilation technique, indicating that “machiya” is equipped to adapt to the climates. The conclusions are provided in
Section 5.
4. Application: Simulations of Vernacular Architecture
Considering the use of empirical equations or CFD simulations to predict the CHTC, previous studies normally focused on simple architectural shapes (such as cubes). However, in the case of specific buildings, the complexity of building geometries has a crucial impact on the flow field and convective heat transfer at surfaces [
31]. Especially in the simulation of vernacular architecture, building geometries can be complicated, and the number of building samples can be large. On the one hand, the CHTC of each surface of the building, which is crucial for computing building energy consumption, should be accurately calculated. On the other hand, architects must perceive the flow field inside and outside the building to validate the passive methods and strategies applied in vernacular architecture, which do not require precise simulations. Therefore, the estimation of the CHTC of buildings and the wind fields outside and inside buildings is actually a trade-off between accuracy and efficiency. According to the results of the above validation, a specific case, that is, the Japanese vernacular architecture “machiya,” is introduced as an application.
4.1. Introduction of “Machiya”
Japan climate has a wide spectrum from summer to winter; however, its vernacular architecture is developed for summer. The doors and partitions between wooden columns are flimsy and lead to poor insulation. Consequently, some portable, local heating devices (such as stoves) are applied to compensate for the heat loss in winter, thus providing an acceptably comfortable indoor environment. By contrast, surviving in summer without active artificial devices is crucial. The city of Nara is crowded and surrounded by mountains, which although protects it from typhoons, gives little ventilation, turning its summers into a torrid, humid and suffocating experience. The “machiya” includes a series of elements and spaces that allow the cross-ventilation of rooms to deal effectively with the high humidity and hotness. A unique space, “doma,” with an earthen floor all through the length of “machiya,” is made in each house to enable good ventilation (
Figure 9a). With no fixed separation between rooms, wind entering from the outside street can freely travel through different rooms. The window at the upper side wall of the house induces air current based on the air buoyancy effect due to the temperature difference, removing indoor hotness and humidity. By the time the wind reaches a small central courtyard, its temperature has risen and is lifted naturally through the courtyard into the atmosphere (
Figure 9b). “Machiya” uses reasonable building shape and space organization and a series of passive strategies to form passive systems. On this basis, the cross-ventilation is strengthened, thereby improving human comfort. Such effects and mechanism can be validated by CFD simulation.
4.2. Simulation for CHTC
As stated in the previous section, the calculation of the CHTC demands the LRNM approach to resolve the viscous layer heat transfer. That is, a fine mesh generation satisfying the requirement to be approximately 1 is necessary. Consequently, a simplified modeling strategy is adopted to avoid high computational cost and time. In addition, the SST k-ω model is preferred according to the validation results.
Figure 10 shows the computational domain and mesh system of the simplified model. Only the external surfaces are modeled, and
at the surfaces is limited to be less than 3. The streamwise, normal, and spanwise lengths of the three-dimensional computational domain are 9, 6, and 15 D, respectively (D = 8 m). The total cell number is approximately 7 million. As reported, the average CHTC value of buildings strongly depends on the ambient wind velocity magnitude [
14]. In this work, the inflow conditions are based on the dominant wind direction and average wind velocity of the local location (Nara, lat: 34.69, lon: 135.80). A turbulent flow (
,
) is imposed at the inlet of the computational domain with a 0° angle. The temperature of the inlet wind flow is 20 °C, with a temperature difference of approximately 10 °C between the wall and the wind flow.
Figure 11 shows the velocity and temperature fields of the “machiya” house at the vertical center plane (y = 0) and the horizontal plane (z = 1/4D). The average CHTCs of each surface are summarized in
Table 4. A comparison with four correlations developed from field measurements (
Table 5) is performed to validate the CFD numerical model. The correlation was developed from flat-plate experiments by McAdams and numerical simulations by Defraeye et al. In the CFD simulations,
corresponds to the wind velocity magnitude assigned at the inlet boundary condition (
).
The CFD simulation obtains a big separation at the roof ridge, developing a large separation region behind the house. The high turbulence intensity and the low wind velocity lead to a weak heat transfer rate [
40], which can be validated by the simulation results. The windward half part of the sloping roof generally has a larger CHTC than that of the leeward half part. In addition, the windward vertical walls have a larger CHTC than that of the leeward vertical walls. The four models developed from field measurements can be separated into two groups: Loveday and Taki [
27] and the ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) task group [
41] is representative of high buildings with six to eight floors, while the MoWITT [
42] and Liu and Harris [
29] group is representative of low-rise buildings.
Figure 12 shows the CHTC profiles with the wind velocity measured at 10 m above the ground level in the upstream undisturbed wind flow (
). The CFD simulation achieves an intermediate value between the two groups of models. The discrepancies in these empirical correlations indicate that although the wind tunnel experiments and field measurements can provide realistic CHTC profiles, they usually lead to case-specific correlations. In terms of large varieties of building geometries and boundary conditions, such correlations cannot simply be applied to each case, which can be confirmed again by the deviation between the CFD simulation and the widely used correlation by McAdams. The numerical correlation of Blocken shows a similar but slightly larger CHTC value than that in the simulation results, which can be partly attributed to the difference between building geometries.
4.3. Simulation for Flow Field
As confirmed in the validation, although simulations using wall functions cannot precisely calculate the CHTC, the solution of velocity and temperature field is highly consistent with the simulations applied with the LRNM approach. Therefore, resolving the fluid flow and temperature distribution of complicated models with low computational cost using wall functions is possible. The RANS model, the RNG k-ε model, is thus selected.
In the simulation of the flow field of “machiya” house, a rough grid system is generated with
> 30. The complicated model and computational domain are shown in
Figure 13. External and internal surfaces are modeled. The size of the computational domain is the same as that of the simplified case but with a total cell number of 5.5 million. The inflow condition is also the same as that in the simplified case, with a uniform flow (
,
) imposed vertically facing the building.
The temperature and velocity fields are shown in
Figure 14. The difference in wind pressure between the windward and leeward regions allows the airflow to pass through the building and remove the hot air from indoors to outdoors. In general, the corner space, which is highly enclosed, easily forms eddy currents, leading to the low efficiency of convective heat transfer and ventilation. The ventilation and heat exchange at a relatively open space is better, especially when the front and rear openings are opposite. In addition, a highly enclosed room with small openings has an even slower wind velocity, lower temperature, and heat transfer rate than that of open space. Even when some doors are closed for privacy reasons, the “doma” space still acts like a wind tunnel that activates cross-ventilation (
Figure 15a). The air is gradually heated when passing through the room. In this process, the air density decreases and the buoyancy effect lifts the hot air, which finally passes through the window on the sidewall (
Figure 15b).
Notably, a deviation exists between the outdoor flow fields of the complicated and simplified models, which is determined by the model fineness and the influence of the indoor flow. Furthermore, the influence of local interferences and microclimatic conditions can be major to simulation results, however it is intentionally ignored in the application study. Although these simplifications will inevitably affect the prediction of the actual CHTC, the accuracy of the simplified model is within an acceptable tolerance according to the validation results.
5. Conclusions
Assessing the thermal and airflow environments in vernacular architecture is of considerable importance in validating and generating climate responsive or energy passive strategies. Specifically, the CHTC at the external surfaces of a building is an important parameter for the accurate numerical simulations of the thermal and airflow environments.
In the validation study, three RANS models and two boundary layer modeling are compared and analyzed in detail. For the CHTC prediction, RANS models applied with the LRNM approach are more accurate than those applied with wall functions. Using wall functions yields a discrepancy of up to 50% compared with the experimental data. The SST k-ω model with the LRNM approach shows better performance in predicting the CHTC than the RNG k-ε and sk-ε models, especially on the windward and leeward surfaces. Given that steady RANS modeling cannot fully consider the flow unsteadiness because of its time-averaged nature, the flow separation and recirculation region cannot be resolved precisely. Consequently, the CHTCs on the lateral and rear surfaces are relatively smaller than those of experimental data, with a discrepancy of approximately 14.3%. Nevertheless, the SST k-ω model applied with the LRNM approach can predict the distribution of the CHTC value over each surface of the building, which is a remarkable improvement to precisely calculating the building energy consumption. Although wall functions exhibit a considerable deviation in CHTC prediction, they still obtain consistent fluid flow, velocity, and temperature fields compared with those of the LRNM, especially in regions far from the wall.
This study developed a methodology involving RANS model selection, boundary layer modeling, and fitness of a target model to predict the convective heat transfer in terms of accuracy and computational cost to demonstrate the application of CFD simulations for CHTC and flow fields. In the simulation of the CHTC, the SST k-ω model applied with the LRNM approach requires a large amount of mesh cells and a short time-step, incurring high computational cost and time. In this case, the building model must be simplified to improve the efficiency of the operation. Furthermore, the fineness of the building model has more influence on the flow field than the CHTC. In the flow field simulation, the RNG k-ε model applied with wall functions is an appropriate choice in terms of its advantage of considerably reducing the computational cost and time. The validation and application indicate that CFD simulation can provide relatively accurate CHTC and flow field for specific and complicated vernacular architecture, laying the foundation for future studies to identify and verify the performance of bioclimatic strategies in vernacular sustainable architecture. These strategies would be committed to optimize the geometries and forms of sustainable architecture, in response to the global background of energy shortage and climate deterioration.