Modelling of Egyptian low-cost-housing natural ventilation: Integration of geometry, orientation and street width optimization

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Modelling of Egyptian low-cost-housing natural ventilation: Integration of geometry, orientation and street width optimization Mahmoud M. AbdelRahmana,⁎, Wael Seddik Moustafab, Osama M. Faragc a b c

Department of Architecture Engineering, Kafrelsheikh University, Kafrelsheikh, Egypt Department of Architecture Engineering, Mansoura University, Mansoura, Egypt College of Architecture and Planning, University of Dammam, Saudi Arabia

AR TI CLE I NF O

AB S T R A CT

Keywords: CFD Low-cost housing Natural ventilation Modelling optimization

Starting from year 1995, 50 thousand low-cost housing units were been proposed to be built In Egypt by 2020 to overcome housing problems, only 60% have been already implemented. Three prototypes are amongst the most frequently built ones by the Egyptian government. In this research, these prototypes have been subject to natural ventilation modelling optimization based on three parameters which are geometry, orientation and the ratio between height and street width H/W using computational fluid dynamics (CFD) standard steady-state K-epsilon turbulence model and standard near wall function by adopting de-coupled approach. This approach indicates that the study is conducted in two levels: Firstly, the resulted pressure values on both sides of each prototype (the windward and the leeward walls) are measured numerically. Secondly, airflow rates inside buildings are calculated using Bernoulli equation. Results have been compared to each prototype's natural-ventilation requirements. In-situ measurements have been conducted to ensure adequate accuracy of calculations. Results of the study showed that further modifications have to be considered in the future configurations of these buildings.

1. Introduction By 2020, it is estimated that 70% of the world will be living in urban areas. This gives rise to some serious environmental, social, political, economic and cultural problems (Santamouris, 2013; Bueno et al., 2014; Molla et al., 2014; Shabana et al., 2015). One of the major consequences of urbanization, is the urban heat island (UHI) which makes air temperature in the densely built-up urban areas dramatically higher than that of the rural surroundings (Bueno et al., 2013; De Ridder et al., 2015). In addition, it has a considerable impact on energy demand, human health and environmental conditions (Oke, 1987; Molla et al., 2014). Many reasons are responsible for UHI one of them is the roughness of the urban surfaces that trap wind in between buildings, and thus decreases heat dispersion (Oke, 1982; Oke, 1987; Mochida and Lun, 2008). UHI has contributed to significant necessity to more thermal comfort energy resources to rebalance the formula especially in hotclimate regions (Crawley, 2008). A study conducted by Santamouris et al. (2001) over more than 30 urban areas indicates that UHI is responsible for doubling cooling loads and tripling electricity consumption in these areas. This problem exacerbates in the developing countries as it is monitored that citizens spend 12% of their income on energy and its services i.e. five times more than the average of developed countries (CICA, 2000; Santamouris, 2005) and 60% of foreign exchange is spent in energy imports in some developing countries (Ali et al., 2016). Furthermore, energy use by nations with emerging economies is forecasted to increase at an average of

⁎

Corresponding author. E-mail address: [email protected] (M.M. AbdelRahman).

http://dx.doi.org/10.1016/j.uclim.2017.08.002 Received 25 March 2017; Received in revised form 14 June 2017; Accepted 3 August 2017 2212-0955/ © 2017 Elsevier B.V. All rights reserved.

Please cite this article as: AbdelRahman, M.M., Urban Climate (2017), http://dx.doi.org/10.1016/j.uclim.2017.08.002

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3.2% and to overtake that of the developed ones. Thus, a study by US Office of Technology Assessment indicates that the efficient use of energy in the developing countries could significantly halve their electricity production especially in a sector that consumes 40% of the world's energy (Der-Petrossian, 1999; Nordberg, 1999). Another study by Ali et al. (2013) demonstrates how quantitative methods for using low carbon society development contribute to mitigation of climate change in Asian developing countries. Natural ventilation is defined as “the flow of outdoor air caused by wind and thermal pressures through intentional openings in the building's shell” (ASHRAE, 2009). The control of airflow into buildings is considered an energy efficient method for both thermal comfort and human health (Dimitroulopoulou, 2012). On one hand by controlling heat dispersion through convection and decreasing sensible heat loads. On the other hand, by the renewal of indoor air and thus removing contaminants and odours. With respect to hot arid climates like that in Egypt, natural ventilation is considered an efficient method for thermal comfort and human health (Nguyen and Reiter, 2014). Numerous researches on natural ventilation for thermal comfort and human health have been conducted based on theoretical (Awbi, 1994; Etheridge and Sandberg, 1996; Awbi, 2003), analytical (Linden, 1999; Coffey and Hunt, 2007), experimental (Straw et al., 2000) and numerical simulations such as Computational Fluid Dynamics (CFD) (Ramponi and Blocken, 2012a, 2012b; van Hooff and Blocken, 2013; Santiago et al., 2014; Yuan and Ng, 2014). Computational Fluid Dynamics (CFD) is defined as the study of fluid motion and heat transfer as well as the interaction of the fluid with solid bodies numerically by solving a number of partial differential equations i.e. continuity equation, energy conservation equations, momentum conservation equations and turbulence models by discretizing a computational domain into tiny finite elements and solving these equations at each point of these elements (Tu et al., 2012; Blazek, 2015). With regard to efficiency, time and cost, CFD is considered a suitable tool for predicting fluid behavior in and around buildings due to the increasing advancement in computers' speed, memory and advanced efficient algorithms. One approach by the Egyptian government has been adopted to overcome housing problems and their consequences started at October 1995. This approach was to build 50,000 low-cost economic housing units with specific designs and geometries. These designs have been approved by the Egyptian Housing and Building National Research Centre HBRC. These units are distributed all over the country and only 60% of these units have been implemented the other 40% units are expected to be built in the next few years (Portal, 2016). They are existed in a number of different sizes and shapes (Portal, 2016). This paper presents a natural ventilation assessment of three of these prototypes based on their geometry, orientation and ratio between height and distance between buildings (H/W), taking into consideration the estimated number of occupants, and the local weather data using CFD. 1.1. Justification of case study selection The selection of these three prototypes is based on the following criteria stated by Gado and Mohamed (2006): 1- These prototypes are amongst the most frequently built prototypes by the Egyptian government, that is a total number of 50,000 were prospected to be built in both old and new cities and about 60% of these housing units have been already implemented (Portal, 2016). Thus, and optimized modelling of these prototypes is justified due to their quantitative volume around the country. 2- These prototypes are meant to be economic and low-income housing. Thus, depending on optimization studies and early stages energy efficiency designs serve this objective. 3- The accessibility of climatic data in addition to safely accessing the site and positioning the measurement instruments is one major fact in choosing case studies. 1.2. Previous studies Pressure-difference based natural ventilation has gained interest of large number of research studies e.g. a study by Asfour (2010) and Asfour and Alshawaf (2015) has investigated the pressure difference on different groups of buildings' configurations. While the study conducted by Zhang and Gu (2008) presented RNG k-epsilon numerical and wind-tunnel investigation of pressure distribution on buildings arranged in staggered way. Another study by Jiang et al. (2003) presented natural ventilation in building using LES model and wind tunnel validation. While urban built-up density i.e. height to street canyon width H\W effect on urban energy balance and thermal comfort has been studied by Wang et al. using Large-Eddie-Simulation approach (Wang et al., 2017). On the other hand, Egyptian prototype housing has been the subject of interest by many researches e.g. a study by Rizk and Henze (2010) investigated air flow around rectangular row of prototype buildings in Egypt. While the study conducted by El-Hefnawi (2000) focused on indoor thermal comfort of Egyptian youth housing without taking into consideration natural ventilation. Also the study by Gado and Osman (2009) and Osman (2011) investigated natural ventilation inside walk-up housing blocks in Egypt. The uniqueness of the current research comes from investigating different parameters of prototype housing such as (geometry, orientation, height to street width aspect ratio) to be integrated with the previous studies. 2. CFD model 2.1. Governing equations Reynolds-Average Navier-Stokes (RANS) turbulence model has been adopted by averaging Navier-Stokes equations' components as shown in the general form:where [1] represents the local acceleration, [2] is the advection term, [3] is the diffusion and [4] is source term. While ϕ, Γ and Sϕ are the property, diffusion coefficient and source respectively and their values change with the type of equation as follows: 2

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Continuity equation ϕ = 1, Γ = 0 and Sϕ = 0. Momentum equation

⎡− ⎢ ⎤ − − ⎢ ⎥, i.e. (time averaged velocity vectors), Γ = v + vT ; Sϕ = ⎢ − ⎦ ⎢− ⎢ ⎣ Energy equation v vT ϕ = T; Γ = Pr + Pr ; Sϕ = ST.

− ⎡u ϕ =⎢ − v − ⎣w

p 1 ∂− ρ ∂x p 1 ∂− ρ ∂y p 1 ∂− ρ ∂z

+ Su′ ⎤ ⎥ + Sv′ ⎥. ⎥ + Sw′ ⎥ ⎥ ⎦

T

Based on standard k-ε model by Launder and Spalding (1972), two transport equations are modelled to define turbulence kinetic energy (k), and its dissipation rate (ε) as follows:

μ ∂k ⎤ ∂k ∂ ∂ ⎡⎛ + (kui ) = μ + t⎞ + Gk + Gb − ρε ⎥ ∂t ∂x i ∂x j ⎢ σ k ⎠ ∂x j ⎦ ⎝ ⎣

(2)

μ ∂ε ⎤ ∂ε ∂ ∂ ⎡⎛ ε ε2 + (εui ) = μ + t⎞ + C1ε (Gk + C3ε Gb) − C2ε ρ ⎥ ∂t ∂x i ∂x j ⎢ σ ∂ x k k ε j ⎠ ⎣⎝ ⎦

(3)

⎜

⎜

⎟

⎟

where k is the turbulence kinetic energy [m2/s2], ε is the turbulence energy dissipation rate [m2/s3]. Whereas, μt is the turbulent viscosity given by:

μt = ρCμ

k2 ε

(4)

while the term Gk represents the generation of turbulence kinetic energy resulted from mean velocity gradients and is given from the equation:

−

Gk = −ρ ui′ u′j

∂uj (5)

∂x i

while the constants: C1ε = 1.44, C2ε = 1.92, Cμ = 0.09, σk = 1.0 and σε = 1.3 which have been experimentally obtained. 2.2. Modelling plants and other obstacles High detailed modelling of trees and small obstacles could be much expensive in terms of computation time and computer resources. Thus, a spatial averaged model of plants and other obstacles can be adopted by adding additional terms to the transport equation e.g. Sϕ in Eq. (1). These terms are: (− Fi) is added to the momentum equation to decrease air velocity based on existence of tree. While (+ Fk) and (+Fε) terms are added to the kinetic energy turbulence equation and the energy dissipation rate equation respectively (Maruyama, 1993; Mochida et al., 2006). 2.3. Near wall treatment Due to the significant impact of the near-wall flow on the fidelity of simulation results as viscous damping reduces the tangential velocity fluctuations, while kinematic blocking reduces the normal fluctuations, it is difficult to resolve the viscous sub-layers near the building walls, and adopting non-slip boundary conditions on the walls isn't possible (Tominaga et al., 2008). Thus, several studies have been conducted to define a suitable treatment using both wall functions and near wall model within a computational domain such as that of Blocken et al. (2007a, 2007b) which discusses the near wall function problem for CFD atmospheric boundary layer (ABL) simulation. A typical computational domain could be classified into 3 main regions: 1) The upstream region, 2) the centre

Fig. 1. Classification of computational domain stream areas for near wall treatment.

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of stream region and 3) the downstream region of flow as shown the following Fig. 1. For the upstream part of the computational domain, the atmospheric inlet boundary layer of the wind profile should be finely representing the roughness characteristic of the part of upstream outside the computational domain. This approach could be satisfied either depending on the roughness length (y0) of the upstream ground, or the power-law exponent in defining the inlet profile (Davenport, 1960; Wieringa, 1992) as follows:

U (y ) =

y u∗ ln y0 k

(6)

where U(y) is the wind speed at height y (m), y0 is the roughness length (m) for this case it is assumed to be 1.0 m, k is the Von Kármán constant (~ 0.41) and u* is the friction velocity (m/s). While for the centre of stream region, modelling boundary layer is a crucial factor for obtaining accurate interpretation of measurements (Barlow, 2014). Thus, buildings surrounding the case study could be explicitly modelled without details e.g. extruded boxes. However, the case study itself should be modelled in details e.g. the exact geometry of the building. While the roughness of the surfaces (e.g. walls, roofs, grass, street…) could be defined in terms of roughness height ks which is typically small e.g. 0–0.1 m (Blocken et al., 2007a). In this study, a standard wall function based on Launder and Spalding (1974) studies have been adopted as it is considered a reliable and frequently used technique and used as a default option in many commercial CFD simulation software such as Fluent, and CFX. 2.4. Discretisation scheme Discretisation scheme could significantly affect fidelity of simulation results. In this respect, many studies have set what is called best practice guidelines (BPG) for an efficient CFD simulation process (Blocken et al., 2012). These guidelines stated that first order discretisation schemes should not be used due to the existence of higher order diffusion calculations. At the same time, fine grid quality should be used for the sake of accurate results. In this study, hexahedral grid has been used inside the domain, however quad meshes have been used for the adjacent near wall cells in addition to using second order discretisation scheme. 2.5. Simulation approach 2.5.1. Coupled vs. decoupled simulation approach In this study, decoupled simulation approach which was introduced by Nore et al. (2010) has been adopted. This means that two separate models of the building were modelled, then the static pressure difference on both sides of the building to be simulated. Then these values are then used to calculate air change rate in each unit. This approach has been selected rather than the coupled one for the following reasons (Nore et al., 2010): 1) it is indicated that the pressure on the windward surfaces and the upstream flow could be accurately simulated using steady RANS modelling with the realizable k-epsilon model and 2) this method is cheaper in terms of computer resources. However regarding the downstream flow, leeward surfaces as well as high Reynolds numbers require further validation. 2.5.2. Static pressure difference Static pressure over building surfaces occurs due to wind blowing on building (ASHRAE, 2009), it depends on wind speed, wind direction, air density and surface orientation. It is almost proportional to the velocity pressure of the undistributed airstream. The wind pressure is given by the Bernoulli equation assuming no height change or pressure losses:

Pw = Cp ρ

U2 2

(7)

where Pw is the wind surface pressure relative to outdoor static pressure in undistributed flow (Pa), ρ is the outside air density (kg/ m3), U is wind speed (m/s) from Eq. (1), and Cp is wind surface pressure coefficient (dimensionless). Accurate determination of Cp can be obtained only from wind tunnel model tests or CFD simulations of the specific site and building or full-scale tests as it depends on building shape, and the influence of nearby buildings, vegetation and terrain features. It is worth mentioning that the wind pressure on building surface is time averaged to at least 60 s due to the high fluctuation values of wind speed and direction as shown in Fig. 8. The relationship that governs airflow rate calculations resulted from static pressure difference on the building walls through large intentional openings is derived from Bernoulli equation.

Q = CD A 2

∆P ρ

(8)

where CD is the discharge coefficient for the window (dimensionless), A is the cross-sectional area of the opening (m2), ρ is the air density (kg/m3) and Δ P is the pressure difference across the openings (Pa). The air change per hour (ACH) term is used to calculate the number of times at which the volume of air totally changed in an hour (h− 1) and is given by:

ACH =

3600 Q V

(9) 4

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Fig. 2. Case study description New Damietta city.

where Q is the airflow rate (m3/s) and V is the volume of the space (m3).

3. Methodology 3.1. Case study description The selected case is neighbourhood in an intermediate city in the north of Egypt called New Damietta as shown in Fig. 2. The residential buildings existed in this neighbourhood are distributed as shown in Tables 1 and 2. Each type of these buildings has been classified based on its orientation, thus the first unit revealed six different orientations, the second unit revealed two orientations due to its symmetric geometry and the third unit revealed two different orientations as illustrated in Table 1. The geometry of each building has been simplified for the sake of performance and following the decoupled approach.

Table 1 The three prototypes of the case study. Case

Geometry

Type 1 (92 units) No. of floors: 5 Apartments per floor: 3 Floor volume: 675 m3 Floor area: 225 m2. Apartment area: 70 m2 Total height: 15 m

Type 2 (37 units) No. of floors: 5 Apartments per floor: 8 Floor volume: 2010 m3 Floor area: 670 m2. Apartment area: 70 m2 Total height: 15 m

Type 2 (39 units) No. of floors: 5 Apartments per floor: 4 Floor volume: 900 m3 Floor area: 300 m2. Apartments area: 70 m2 Total height: 15 m

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Table 2 Distribution of Prototypes in the neighbourhood. Prototype 1 distribution

Prototype 2 distribution

Prototype 3 distribution

3.2. Variables declaration In this study, three main variables has evaluated, these are: (1) Building geometry, (2) Building orientation and (3) Building height to street width H/W. These variables have been declared as follows:

3.3. Methodology breakdown Each orientation of the units of the three types which are illustrated in Table 1, has been evaluated in a context of surround buildings based on the ratio between the height of the building H, and the width of the street W i.e. (H/W = 1.5, H/W = 1 and H/ W = 0.5) as shown in Fig. 3. Then, static pressure on the wind ward and lee ward surfaces of each building has been measured and used to evaluate the airflow rate Q in each unit based on Bernoulli Eq. (8). Then, results have been compared to the natural ventilation requirements in residential buildings in Egypt as stated by the Egyptian code for optimizing energy efficiency in residential buildings which indicates that the minimum required natural airflow rate in residential buildings to be: 3 L/s/person in bedrooms and living rooms, and 14 L/s/person in bathrooms and kitchens. While based on activity, the required airflow rate should range from 1.3 to 2.6 L/s per m2 for sitting and light activity. While the air changes per hour ACH rate in general rooms should be between 4H− 1 and 7H− 1. As for ASHRAE standard 62.2, the minimum required airflow rates can be calculated using the following equation.

Q = 0.18Afloor + 12.6(Nbedrooms + 1)

(10)

where Q is airflow rate (m /h), Afloor is the floor area (m ), and Nbedrooms is the number of bedrooms 2

2

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Fig. 3. Each case is positioned amongst blocks of buildings varied in Height to street width ration (H/W).

3.4. Assumptions of the study It is assumed that the maximum number of occupants in one residential unit to be 5 i.e. 2 adults and 3 children. In addition, it is assumed that each room has an opening to the outside with area 1 m2 for design purposes, and the discharge coefficient of each opening CD is assumed to be 0.63 that is, a square window with fly mesh as shown in Fig. 4. 4. CFD simulation process 4.1. Boundary conditions The inlet of the domain is a logarithmic average wind speed profile derived from Eq. (1) assuming that the roughness length of the terrain z0 = 1.0 m i.e. (Regular large obstacle coverage, suburb) and depending on the meteorological station at 10 m height which is 4 m/s. On the other hand, this simulation is based on standard k-ε turbulence model in which k is the turbulence kinematic energy given by:

k = 1.5(IU U)2

(11)

where IU is the inlet longitudinal turbulence intensity and its value ranges from 30% at z = 2 m to 5% at the height gradient and U is the longitudinal inlet velocity. The turbulence dissipation rate ε is calculated from:

ε=

∗ (U ABL )3 k (z + z0 )

(12)

∗

where UABL is the atmospheric boundary layer friction (ABL) which is related to the logarithmic inlet profile, k is the Von Kármán constant (~ 0.41), z is the height and z0 is the roughness length 1.0 m. the resulted inlet profile, turbulence kinematic energy and turbulence dissipation rate are shown in Fig. 5. 4.2. Computational model and domain A solid geometry of each case has been built, then placed in a box-shaped computational domain, the inlet boundary condition is the velocity logarithmic profile shown in Fig. 5. The outlet surface has been set to gauge pressure with 0 Pa value, and the other 3 surfaces of the boundary conditions have been set to slip\symmetry. The material of the domain has been set to be air with 1.2047 kg/m3 density and the distance from the building to the domain sides and the inlet is five times the height of the building, and the distance from the building to the outlet of the domain is fifteen times the height of the building as stated by Hall (1997) (Fig. 6). Autodesk Simulation CFD 2015 code is used to solve the k-epsilon steady- state turbulence model. The average finite elements' number for each case is 2,000,000. And the convergence time is about 1 h for each case. A 30 total number of cases evaluated that is (3 × 6 of the first prototype, 3 × 2 of the second prototype, and 3 × 2 of the third prototype). The machine used in the simulation process is Intel Core I7 processor i.e. (4 cores and 8 threads) with 64-bit architecture support which, in this case, is considered suitable

Fig. 4. A typical window with fly mesh used in youth housing prototypes with size 1 m2 and 0.63 discharge coefficient.

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y (m)

55 50 45 40 35 30 25 20 15 10 5 0

-10

U(y) K E

0 10 U(y), K, Epsilon

20

A

B

Fig. 5. [A] Domain inlet boundary conditions and [B]wind data from the nearest weather station at 10 m height.

Outlet gauge pressure Symmetry Inlet – normal velocity

A- Boundary conditions

> B- Meshing

> C- Simulation

Fig. 6. CFD domain, meshing and boundary conditions.

for the study as stated by Jamshed (2015) in addition to 16 GB RAM as it is recommended to set 2 GB of ram for each core (Jamshed, 2015).

4.3. Case study validation Validation of the case study is based on 10 min time averaged in-situ measurement of air velocity and static pressure. The loggers have been located in two places. Firstly, on the top of building Fig. 7-A and on building wall Fig. 7-B. The first logger is used to measure free air stream velocity. While the other is used to measure static pressure at specific point on the wall taking into consideration closing windows to mimic the numerical simulation case. A digital anemometer has been used to measure wind speed and gauge pressure Fig. 7-C. The resolution of the instrument is 0.1 m/s while the accuracy ranges between ± 5%. Measurements were conducted for a diurnal cycle in a typical summertime day. Fig. 8 shows a plot of the wind speed measured by data logger1, and static pressure measured by data logger2.

A

B Fig. 7. Data logger located on top of one building and on the wall of other building.

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Fig. 8. Field measurements taken from data loggers. Above: free stream wind speed, and below: static pressure on building wall.

4.4. Meshing grid verification As stated by the BPG, a grid validation procedure should be reported to ensure the validity of the simulation results. Thus, a grid validation has been performed to ensure accurate simulation results. This validation took place by comparing reference points in the experimental study to those of a coarse grid and fine grid as shown in Fig. 9.

5. Results The next tables show the results of each case labelled as follows: Each case has a label of 3 digits, for example: (2-1-3), the first digit stands for the prototype number, whereas the second digit stands for the orientation of the prototype i.e. (6 orientations for the first prototype, 2 orientations for the second prototype, and 2 orientations for the third prototype), and the last digit stands for the ratio H/W i.e. ([1] indicates that H/W is 1.5, [2] indicates that H/W = 1, and [3] indicates that H/W = 0.5). Thus for example case 2-1-3 refers to the second prototype, the first orientation and the ratio H/W = 0.5. On the other hand, results shown in the following tables are based on inlet boundary conditions from Eqs. (11) and (12) where inlet wind velocity is calculated from wind frequency rose shown in Fig. 5-B i.e. hourly averaged annual cycle obtained from the nearest weather station at 10 m height. Then the actual velocity is corrected using the velocity logarithmic profile based on surface roughness length shown in Fig. 5-A. Validation of these results followed previous studies conducted by Ramponi and Blocken (2012a, 2012b) (Li and Delsante, 1998; Heiselberg et al., 2004; ASHRAE, 2009; Ramponi and Blocken, 2012a, 2012b; Montazeri and Blocken, 2013).

Fig. 9. Meshing grid verification.

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5.1. First prototype

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5.2. Second prototype

5.3. Third prototype

6. Discussion As shown in the previous results, the first prototype has indicated high ACH results for cases with H/W = 1 and H/W = 0.5. However, for cases where H/W = 1.5, this values didn't meet the requirements except for case 1-5-1 as it exceeds the maximum required ACH values (Fig. 10). However the second prototype demonstrated ACH values lower than the minimum required for almost each case except for case 21-3, the reason behind that is the large floor area which made the number of air changes per hour very low although the airflow rates are the highest. The third prototype however, has shown suitable ACH values for cases where H/W = 1 and 2 but showed lower ACH 11

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ACH

Minimum required ACH = 4 h-1 Maximum required ACH = 7 h-1

case 1-6-3

case 1-6-2

case 1-6-1

case 1-5-3

case 1-5-1

case 1-5-2

case 1-4-3

case 1-4-2

case 1-4-1

case 1-3-3

case 1-3-2

case 1-3-1

case 1-2-3

case 1-2-2

case 1-2-1

case 1-1-3

case 1-1-2

case 1-1-1

Air Change Per Hour ACH [h-1]

M.M. AbdelRahman et al.

Fig. 10. Air change per hour values for the first prototype cases.

values for cases where H/W = 1.5 (Fig. 11). The airflow rates for all the cases are shown in Fig. 12. This study can be adobted for future prototype housing planning, however, extra invistigation about infilteration should be conducted. Infiltration is a significant contributor to the air change in residential buildings. Air leaking data in Egyptian residential buildings are absent. It is required to be measured to have a concise insight about the actual required flowrates.

7. Conclusion This paper introduces a methodology for assessing natural ventilation-based planning of prototype residential buildings in Egypt to be available to urban planners in the future neighbourhood planning in two main aspects: Firstly, in choosing the appropriate prototype and, secondly, in planning spacing between prototypes based of building height and wind flow direction. These types of buildings are built by the government in mass production and are considered economically efficient dwelling. The study has been conducted for the northern coastal climate region in Egypt where rapid growth in urban low-cost housing in this area is expected in the next few years, and this what gives this research significance. This research on the other side, highlights the reliability of CFD simulations in predicting natural ventilation, and thus human health and energy efficiency, albeit it requires validation using more empirical or analytical steps to assure the credibility of results and narrow the error range. Future work involves evaluating natural-ventilation thermal comfort in these prototypes. Besides conducting this research for prototypes in other climate regions in Egypt. The aggregation of these researches should be seriously taken into account while planning the future neighbourhoods. More attention should be paid to infiltration data in these buildings as it would highly impact the results and it must be empirically measured.

Appendix A. Supplementary data

case 3-2-3

case 3-2-2

ACH Minimum required ACH = 4 h^-1 Maximum required ACH = 7 h^-1

Fig. 11. Air change per hour values for the second and third prototypes.

12

case 3-2-1

case 3-1-3

Maximum required ACH = 7 h^-1

case 3-1-2

Minimum required ACH = 4 h^-1

Third prototype

case 3-1-1

case 2-2-3

case 2-2-2

case 2-2-1

case 2-1-3

case 2-1-2

ACH

Air Change Per Hour ACH [h-1]

Second prototype

case 2-1-1

Air Change Per Hour ACH [h-1]

Supplementary data associated with this article can be found in the online version http://dx.doi.org/10.1016/j.uclim.2017.08. 002. These data include the Google maps of the most important areas described in this article.

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Fig. 12. Air change per hour values for all cases.

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