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Evaluation of building arrangement on natural ventilation potential in ideal building arrays
Evaluation of building arrangement on natural ventilation potential in ideal building arrays
Yawen, Zhong; Wei, Yin; Yonghan, Li; Xiaoli, Hao; Shaobo, Zhang; Qiaoyun, Han; Shuangping, Duan
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING https://doi.org/10.1080/13467581.2023.2191680 URBAN PLANNING AND DESIGN Evaluation of building arrangement on natural ventilation potential in ideal building arrays a a,b,c a a,b a a Zhong Yawen , Yin Wei , Li Yonghan , Hao Xiaoli , Zhang Shaobo , Han Qiaoyun and Duan Shuangping a b School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, China; Hunan Engineering Research Center for c d Intelligently Prefabricated Passive House, Xiangtan, China; College of Architecture, Hunan University, Changsha, China; School of Architecture and Energy engineering, Wenzhou University of Technology, Wenzhou, China ABSTRACT ARTICLE HISTORY Received 16 January 2023 Nowadays, the process of urbanization is accelerating, and the density of buildings is increas- Accepted 13 March 2023 ing rapidly. Natural ventlation is an energy-saving way of building ventilation and to solve the issue of building overheating. The buildings are all considered as cubes and their arrays are KEYWORDS evenly distributed. The results from nearly 100 cases show density is the main factor in uniform Building energy arrays and not much difference between the staggered and normal arrangements if consider conservation; wind a year. The method can reflect the ventilation capacity of the buildings, which is somewhat environment; natural inverse to the air age but different meaning. It can be used to guide the layout and planning in ventilation; wind pressure ventilation; city planning; early design stage. computational fluid dynamics (CFD) 1. Introduction simulation results for the average velocity and turbu- lent kinetic energy. Van Hooff and Blocken (2020) Building energy-saving technologies can improve found that the RNG k-ε turbulence model in predicting energy efficiency and reduce carbon emissions, mixing ventilation flows; differences in mean velocity among which natural ventilation is an effective were generally within 10–20%, while 80% of the pre- means. Natural ventilation can improve indoor air dictions of TKE were within 30% from the measure- quality and reduce air conditioning energy consump- ment results. Hang et al. (2013) observed that the tion. (Wang et al. 2022; Zhang et al. 2022) Its driving standard k–ε outperforms other RANS models in air- forces are divided into buoyancy (Wei et al. 2010) and flow simulations of street canyons. Shirzadi, wind pressure ventilations. (Zhang et al. 2021) The Naghashzadegan, and Mirzaei (2018) quantified the potential for wind pressure ventilation is mainly deter- limitations of the RANS model in the application of mined by climatic conditions and building design; the ventilation in highly congested urban areas and latter includes building height, density, and arrange- observed that the accuracy of the RANS model was ment. (Yin et al. 2010) In this study, we discuss the better when the building densities were between 0.25 effect of building arrangement on the natural ventila- and 0.4. Tominaga et al. (2004) compared the standard tion potential (VP) of wind pressure. There are three k–ε model, revised k–ε models, differential stress main research methods for building wind environ- model, Large Eddy Simulation, and direct numerical ments: field tests, wind tunnel experiments, and com- simulation with the experimental results and observed putational fluid dynamics (CFD). that the prediction of the horizontal scalar velocity The CFD simulation method, whose accuracy has distribution of pedestrians was consistent with the been demonstrated in numerous studies, is widely measured results in the actual building group, except used in the study of wind environments around build- in the wake region and far away outside the area of the ings. In a study of complex urban canopies, Ricci et al. target building because the grid resolution was not (2020) observed that the fluid field under different sufficiently fine. Montazeri and Blocken (2013) studied Reynolds-averaged Navier – Stokes (RANS) equation the influence of balconies on the wind pressure coeffi - models can differ by 20–60%. van Hooff, Blocken, and cient and observed that for mid-rise buildings with or Tominaga (2017) observed that all RANS models without balconies, the RANS simulation was biased underestimated the turbulent kinetic energy, however, when the wind was oblique. All other cases were con- large eddy simulations (LES) provided better sistent with the wind tunnel experiments. Therefore, CONTACT Yin Wei email@example.com School of Civil Engineering, Hunan University of Science and Technology, Xiangtan411201, China © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 Z. YAWEN ET AL. the above study shows that the CFD simulation ignored in densely built mountain areas, and the ven- method can reproduce the wind tunnel experimental tilation capacity of open space in urban areas is better results and can be used to predict the airflow field than that in narrow areas. Based on three buildings, around the building. Dai, Mak, and Ai (2019) found that the existence of For a single building, there are various factors affect - upstream buildings does not necessarily make the ing natural ventilation, such as outdoor wind direction wind environment of downstream buildings worse. and wind speed, building opening area and position, Hanna et al. (2002) observed that compared with stag- outer surface protrusion, and roughness. Derakhshan gered arrays, the canyon effect of a normal arrange- and Shaker (2017) explored the effect of the opening ment is more obvious. Al-Sallal, AboulNaga, and aspect ratio on indoor ventilation and observed that Alteraifi (2001) observed that changing building when the outdoor wind direction was greater than 45°, heights upstream and downstream had a greater the ventilation volume was unrelated to the window impact on the airflow path and velocity in urban size. Mattsson (2004) studied the influence of terrain, spaces. Wang and Ng (2018) observed that the ventila- surrounding environment, and wind speed on the tion effect in rectangular buildings was better than ventilation rate. For high-rise buildings, Liu et al. that in square buildings. Arkon and Özkol (2014) (2019) observed that the ventilation is extremely sen- observed that the block shape demonstrates sitive to the changes in wind conditions, followed by a significant influence on the wind speed at pedestrian the difference in ventilation mode, window type, and heights in a survey. Hang, Li, and Sandberg (2011) window orientation. Chand, Bhargava, and Krishak observed that density has a greater impact on high- (1998) observed that balconies changed the wind- rise building arrays and that wider streets can improve pressure distribution on the windward wall. Lee et al. ventilation. (2015) observed that the change in the shape of the For the building layout, if the shape, porosity, and outer louver had a significant impact on the natural surface roughness of the building are discussed, the ventilation rate. Ok, Yasa, and Özgunler (2008) sug- problem will become very complicated, so the building gested that openings located on perpendicular sur- model needs to be simplified. In wind tunnel experi- faces increase the airflow velocity within courtyards. ments, Buccolieri et al. (2019) simplified buildings as Kindangen, Krauss, and Depecker (1997) studied the cubes to ignore the effects of building shape and sur- effect of roof shape on indoor ventilation and face roughness. In this study, such an ideal building observed that enhancing negative pressure can pro- model is also taken. At the same time, when there are mote indoor air circulation. The design of a single more building models, the method of evaluating the building mainly affects indoor ventilation and has less building ventilation potential by making the difference impact on the outdoor wind environment. The out- in wind pressure between the two sides of the building door wind environment is mainly affected by local used in the previous study is considered to be cumber- meteorological data and building arrangements. some. We propose a method for evaluating the build- The proliferation of density in urban residential ing ventilation potential based on the standard areas has led to many wind environment problems in difference of wind pressure on the building façade urban residential areas, such as poor ventilation and and explored the effects of density, staggered and difficulties in pollutant diffusion. Some studies have normal arrangement, and potential of wind-force nat- shown that reasonable spatial design and configura - ural ventilation. tion of urban residential areas can significantly In the study, we first introduced the building com- improve the wind environment of residential areas. plex model and CFD settings and compare and vali- Zhen et al. (2019) and Asfour (2010) believed that the date them with the wind tunnel experimental data. spatial form of urban residential blocks was the main Thereafter, the potential evaluation method based on factor affecting the wind environment around build- the standard deviation of wind pressure was proposed. ings. Kim, Yoshida, and Tamura (2012) observed Next, the impact of building density, building arrange- through wind tunnel experiments that when the build- ment, overhead, and year-round weather data were ing density was high, the average wind speed between analyzed. Finally, a comparison with the conclusions buildings decreased significantly and turbulence inten- of previous studies was presented. sity increased significantly. Through wind tunnel experiments, Tsutsumi, Katayama, and Nishida (1992) observed that the spacing in buildings have a greater 2. Building models and methodology impact on the building wind pressure coefficient, and 2.1. Building model with the change in building density, the wind pressure coefficient distribution on the building surface will be To measure the accuracy of wind tunnel experiments, larger in the central area than in the surrounding area. Buccolieri et al. (2019) and Hang and Li (2010) estab- Ling Chen, Lu, and Yu (2017) found that the influence lished the ideal building group arrays where each of terrain factors on building ventilation cannot be building was simplified as a cube, whose overall JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3 in the vertical direction was expressed as a power function, as shown in Equation (1). UðzÞ z 0:16 ¼ ð Þ (1) UðHÞ H where U(z)(m/s) and U(H)(5.2 m/s) are the average wind speeds at the building height z (m) and the reference height H = 0.06 m, respectively. The vertical distribution of the turbulent intensity of the incoming Figure 1. Building model. flow was expressed as a power function, as shown in Equation (2). dimensions were 0.06 × 0.06 × 0.06 m (length × � � 0:06 IðzÞ z width × height), as shown in Figure 1. In this study, � (2) IðHÞ H we built a CFD model based on the aforementioned model and its dimensions. where I(z) (m/s) and I(H) (27.6 m/s) are the turbulence intensity at building height z (m) and reference height H (m), respectively. The average incoming velocity and 2.2. CFD setting turbulence intensity formulas were programmed into a user-defined function (UDF) and imported into 2.2.1. Fluid domain Fluent. The Reynolds number is 20,800, which reaches As suggested by Tominaga et al. (2015), Blocken, the fully turbulent state. Stathopoulos, and Carmeliet (2007), and Franke et al. The number of grids was more than 6 million, (2007), the flow field was set as follows. The distance and the simulation time using LES was about from the building to the top and sides of the computa- a month for each case by Intel Xeon 4-core proces- tional domain was 5 H (H means the building height). sor (32-core when hyperthreading) is enabled. On The distances of the outlet boundary downstream of the second hand, Tominaga et al. (2004) found in the building and inlet boundary upstream of the build- a similar case that the difference between RANS ing are 15 H and 5 H, respectively. The resulting com- and LES within the buildings complex is small. putational domain size was 1.98 × 1.38 × 0.36 m Their difference mainly occurs in the wake, which (length × width × height), as shown in Figure 2. is not considered in the study. Considering the time cost and accuracy, this paper uses RANS for simula- 2.2.2. Boundary conditions and turbulence model tion. The building model of Hang et al. (2013) is In the boundary conditions of ANSYS Fluent 2021, the similar to that used in this study. Additionally, and ground and building surfaces were defined as walls, Hang et al. (2013) observed that the simulated data the top and sides of the fluid domain exhibited sym- from the standard k-ε model in the RANS model metric boundary conditions, the outlet was set as the were closest to the experimental values. Therefore, pressure outlet, and the inlet was set as the velocity this paper also uses the Standard k-ε model to inlet. In the wind tunnel experiment (Buccolieri et al. simulate. The control equations were discretized 2019), the distribution of the measured inflow velocity by the finite volume method under the second Figure 2. Building array and computational domain. (a) Computational domain, (b) Building array 7 × 7. 4 Z. YAWEN ET AL. order, the SIMPLEC scheme was used to couple 2.3. Comparison with wind tunnel data pressure and velocity, and all residuals were at 2.3.1. Front and rear pressure differences in middle −5 least 10 to convergence, and the computation row time for all models is approximately 6 months. As shown in Figure 5, the dimensionless coefficient of pressure difference (C ) between the windward and PD 2.2.3. Grid and sensitivity analyses leeward directions in the middle row of the 7 × 7 array A schematic of the 7 × 7 array is shown in Figure 3(a), was defined using Equation (3) (Buccolieri et al. 2019). and its meshing method is discussed in this section. � � P P According to the surface mesh technique of van Hooff windward leeward C ¼ ; (3) PD and Blocken, (2013), the grids were hexahedral struc- P D isolatedcube tured grids with the quality ranging between 0.95 and where P and P represent the average pres- windward leeward 1. To control the number of grids, the surface and sures on the windward and leeward sides of the cube, corners of the building were densified, as shown in respectively, and P is the cube pressure dif- D isolatedcube Figures 3(b,c,). ference of an isolated cube. The simulated and experi- Seven densities were set: 0.01 m (minimum grid mental values of C , which are very similar, are shown PD size; total number of grids = 140,000), 0.005 m in Figure 6. (1 million grids), 0.001 m (1.9 million grids), 0.0005 m (4.4 million grids), 0.0003 m (6.8 million grids), 0.00008 m (10.2 million grids), and 0.00005 m 2.3.2. Pressure distribution on cubes (15 million grids). All the grid magnification factors In this section, we further analyze the accuracy of wind were less than 1.3. pressure on the windward side of the 1st, 4th, and 7th Figure 4 shows the average wind pressures of all cubes in the middle column of Figure 5. Figure 7(a) roofs for the seven mesh densities. It can be observed shows the wind pressure along the vertical central line that with an increase in grid density, the average wind of the three cubes. The average error between the CFD pressure on the top surface of all blocks tends to be simulation and wind tunnel experimental data stable. When the total number of grids exceeds (Buccolieri et al. 2019) was 11%. Figure 7(b) shows 4.4 million, the average wind pressure changed the wind pressure along the horizontal central line slightly. Considering the time cost, we selected a grid with an average error of 15%. The error of the two- density of 0.0003 (total number of grids = 6.8 million) line line mainly originates from the first cube, and the for the simulation. simulated value is smaller than the experimental value. (b) Grid between building (a) Building array 7 × 7 and distance (c) Mesh of the entire area (d) Mesh density at the edge of the building Figure 3. Meshing of fluid domain. (a) Building array 7 × 7 and distance, (b) Grid between building, (c) Mesh of the entire area, (d) Mesh density at the edge of the building. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5 -2.5 experimental results (Buccolieri et al. 2019). As shown Average wind pressure in Figure 8, it can be seen to be very similar. -3.0 2.4. Potential assessment method based on -3.5 standard deviation of wind pressure In previous studies, the pressure difference between -4.0 two facades was used to evaluate the wind pressure VP, as reported by Buccolieri et al. (2019) But during the building design process, the architects want to -4.5 have openings on the various facades for ventilation without restrictions. Therefore, this study proposes to -5.0 measure the wind-ventilation potential of the building 0.14 1 1.9 4.4 6.8 10.2 15 based on the pressure difference of the four facades on Total number of grids million the cube. Figure 4. Average wind pressure on roofs. 2.4.1. Definition of ventilation potential based on standard deviation of wind pressure In CFD software, such as Ansys fluent, the four sides of each block need to be defined separately. When out- putting data after simulation, you need to select the output one by one separately, and then perform pres- sure data processing. When the number of models is too large, these steps will be cumbersome. A new evaluation method for ventilation potential is pro- posed. It only needs to set all four facades of the model as a whole, and the pressure data can be directly output after the simulation. The data proces- sing steps are reduced, which is suitable for the situa- tion of a large number of simulation models. Therefore, in this study, we intend to use the wind pressure Figure 5. Middle column of a 7 × 7 matrix. standard deviation of all the facades to evaluate the natural VP. Specifically, the wind pressure ventilation 1.2 capacity is discussed based on the nonuniformity of CFD the outer surface of the building. The standard devia- Exp 1.0 tion of the wind pressure for each building, P (Pa), is 0.8 shown in Equation (4) (Ansys, Inc. 2021). rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 N 0.6 2 P ¼ ðP PÞ ; (4) σ i i¼1 0.4 Where N is the pressure point on the surface, the number of pressure points is controlled by the number 0.2 of grids divided, and the specific value location is 0.0 explained in Section 126.96.36.199 of ANSYS Fluent- Theory Guide-Release 2021R1. P is the wind pressure -0.2 (Pa) at position i, and P is the average value of all 0 1 2 3 4 5 6 7 8 points. ANSYS Fluent can directly calculate the stan- Cube dard deviation of the wind pressure on specified sur- Figure 6. Dimensionless pressure difference between wind- faces, which is very convenient for calculating the VP of ward and leeward sides. buildings with any shape. However, the changing trends of the two were similar, 2.4.2. Comparison with “average pressure and this error did not affect the relativity of the results. difference” Meanwhile, the wind pressure distribution on the In the study of Tsutsumi, Katayama, and Nishida (1992), building surface of the 1st, 4th, and 7th cubes in the the “average value of wind pressure coefficient differ - middle column were compared with the wind tunnel ence” was used to evaluate the ventilation potential of Average wind pressure (Pa) PD 6 Z. YAWEN ET AL. First cube Fourth cube Seventh cube First cube Fourth cube Seventh cube CFD CFD Exp Exp 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Location Location (a) Middle vertical line (b) Middle horizontal line Figure 7. Comparison of wind pressure on windward facades of buildings. (a) Middle vertical line, (b) Middle horizontal line. Figure 8. Comparison of building surface wind pressure of buildings. (a) Wind pressure on building surface in wind tunnel experiment, (b) Wind pressure on building surface in CFD. the building. If four facades are considered, their and 7 × 7 arrays, their “wind pressure difference ðP Þ” method approximates equation (5). versus our “wind pressure standard deviation(P )” is shown in Figure 9. � � � � � � P þ P þ P þ P þ P þ P B D B C B E C E C D E D P ¼ (5) m Obviously our “wind pressure standard deviation(P )” is proportional to the “wind pressure In which, P is the average wind pressure, P the m B D difference ðP Þ”. For further proof, we took the 3 × 3, wind pressure difference(Δ p) between the windward 5 × 5 and 7 × 7 arrays as the object, and explored the side and the leeward side, P the Δp between the B C three wind directions of 0°, 22.5° and 45°. Table 1 windward side and the left side, P the Δp between shows the correlation between the two ventilation B E the windward side and the right side, P the Δp potential evaluation methods: “wind pressure C E difference ðP Þ” and “wind pressure standard between the left side and the right side, P the Δp m C D deviation(P )”. Their correlation coefficient r is between the left side and the leeward side, P the Δp σ E D Equation (6). between the right and leeward sides. In the 3 × 3, 5 × 5, JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7 Figure 9. “Wind pressure difference( P )” VS “Wind pressure standard deviation(P )”. m σ Therefore in the next section, the standard devia- ðP P ÞðP P Þ mi m σi σ i¼1 r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (6) P P tion of the wind pressure on the four facades is taken 2 2 n n ðP P Þ � ðP P Þ mi m σi σ i¼1 i¼1 as the wind VP of the building, which is referred to as the VP. Building density, alignment, lift, and meteoro- Where r is the correlation coefficient, P is the aver- mi logical data were analyzed for their impact on the VP. age wind pressure of the i building in the building group,P is the average value of the average wind pressure of all buildings, P is the standard devia- σi 3. Result analysis tion of wind pressure for i building. P is the aver- 3.1. Influence of building density age of wind pressure standard deviations for all buildings. The building arrays are concentrated in an area of It can be seen from Table 1 that in different wind 13 H × 13 H, and four uniform densities of 3 × 3, 5 × directions, the two ventilation potential evaluation 5, 7 × 7, and 9 × 9 are discussed. The spacing para- methods of “wind pressure difference( P )” and “wind meters are listed in Table 2, and the respective VPs pressure standard deviation(P )” have very good corre- are shown in Figures 10, 11, 12, and 13. The aver- lations. The lower correlation coefficients are all above age VP (VP ),the standard deviation of the VP (VP ) a sd 0.95. The variation trend of the “wind pressure stan- , and ratio of the second data point to the first dard deviation (P )” ventilation potential evaluation data point, is the relative inhomogeneity of the VP method is very close to that of the “wind pressure value of each building are shown below each fig - difference ðP Þ”. Therefore, in the block building ure. The larger the VP /VP value, more uneven is m sd a model, it is reliable to use the “wind pressure standard the VP. The average VP (VP ) and the standard deviation” to initially evaluate the ventilation potential deviation of the VP (VP ) are calculated in sd of the building array. Equations (7) and (8). Table 1. Correlation coefficient r between “wind pressure difference( P )” and “wind pressure standard deviation(P )” in m σ Table 2. Building density and spacing. different wind directions. Cubes Density Spacing Center distance between buildings Arrangement 0° 22.5° 45° 3 X 3 0.028 5H 6H 3×3 0.99 0.96 0.95 5 X 5 0.110 2H 3H 5×5 0.99 0.99 0.98 7 X 7 0.250 1H 2H 7×7 0.99 0.98 0.98 9 X 9 0.440 0.5H 1.5H 8 Z. YAWEN ET AL. Figure 10. VP and uniformity of a 3 × 3 building array. Figure 11. VP and uniformity of a 5 × 5 building array. P As shown in Figure 10 (3 × 3, 5 H spacing), for the 0° x¼1 VP ¼ (7) wind direction, the VP of the first row on the windward side is 1.5 times greater than that of the second row. However, the VP of all buildings was similar when the rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n wind directions were 22.5° and 45°. VP ¼ ðP VP Þ (8) sd σ a x¼1 n As shown in Figure 11 (5 × 5, 2 H spacing), when the wind direction is 0°, the VP of the first row of buildings Where n is the number of all buildings. P (Pa) is the σ on the windward side is 3.1 times larger than that of standard deviation value of wind pressure, which is the second row. When the wind direction is 22.5°, the also the value of natural ventilation potential, see ratio of the first row to the second row on the wind- Equation (4). ward side is 1.7. When the wind direction is 45°, the JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 9 Figure 12. VP and uniformity of a 7 × 7 building array. ratio of the first row to the second row is 1.4. However, As shown in Figure 13 (9 × 9, 0.5 H spacing), for VP of the first row is reduced by a factor of 0.93 times the 0° wind direction, the VP of the first row of when the wind direction is 0°. buildings is 13.7 times larger than that of As shown in Figure 12 (7 × 7, 1 H spacing), for the 0° the second row. When the wind direction is 22.5°, wind direction, VP of the first row is 5.3 times larger the ratio of the first row to the second row is 7.7. than that of the second row. When the wind direction Additionally, the VP of the first row of buildings is is 22.5°, the ratio of the first row to the second row is 0.93 times of that when the wind direction is 0°. 3.6. Simultaneously, the VP of the first row of buildings When the wind direction is 45°, the ratio of the first is 0.96 times of that when the wind direction is 0°. row to the second row was 4.4, and the VP of the When the wind direction is 45°, the ratio of the first first row of buildings was 0.75 times of that when row to the second row is 2.6. Moreover, the VP of the the wind direction is 0°. first row of buildings is 0.84 times of that when the It can be observed that when the density of the wind direction is 0°. building group increases, the difference in VP between Figure 13. VP and uniformity of a 9 × 9 building array. 10 Z. YAWEN ET AL. the front and rear rows is greater. When the wind Compared with Figure 12 (normal alignment), VP of direction changes to a certain angle, the difference in staggered 0.5 H and 1 H increased by 3% and 22%, the building VP will be smaller. Simultaneously, VP in whereas VPa/VPsd decreased by 23% and 11%, respec- the first row decreases slightly. tively. The uniformity of the VP increased after the Because VP cannot be 0, nor can the building den- staggering. sity be 0; a power function was used to fit the building When the wind direction is 0°, as shown in Figure 15 density to the VPa, as shown in Figure 14. Obviously, (staggered 0.5 H) and Figure 16 (staggered 1 H), VP of the the trends for the three angles in Figure 14 (a) are first row of buildings is 1.8 and 1.5 times larger than that similar, and their average curve is shown in Figure 14 of the second row, respectively. This is much smaller than (b). The two formulas (average of three angles) can be that observed in Figure 12 (normal alignment) by a factor used to estimate the VPa in building planning. of 5.3. The building VP of the first row is 3.6 and 3.4 times larger than that of the third row. This is much smaller than that observed in Figure 12 (normal alignment) by a factor 3.2. Staggered VS normal arrangement of 6.1. The first row is 6.2 and 5.1 times larger than the fourth row, respectively, which is slightly smaller than that In this section, we use a 7 × 7 array to analyze the effect of observed in Figure 12 (normal alignment) by 7.1 times. the staggered arrangement. The staggered arrangement The results for 22.5° and 45° wind directions were also was further divided into 0.5 times the H spacing similar. It can be observed that the staggered (Figure 15) and 1 times the H spacing (Figure 16). Figure 14. Building density, VP, and VP inhomogeneity. Figure 15. Staggered arrangement, 7 × 7 array, 0.5H spacing. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 11 Figure 16. Staggered arrangement, 7 × 7 array, 1H spacing. arrangement has an obvious improvement effect on the 39%, respectively. VP of the second line and the following lines are essentially unchanged. VPa of the building com- first three rows. Moreover, after staggering, the two col- plex increased by 13%, 20%, and 28%, respectively. umns of buildings in the edge area improved by approxi- However, the nonuniformity (VPa/VPsd) increased by mately 2 times. 8%, 11%, and 12%, respectively. Overall, staggering can slightly improve the natural It can be observed that the overall rise-up only VP of the building array with an increase in uniformity. enhances the ventilation of the first row, but the improve- ment in the rear VP is small. Specifically, the distribution of the natural VP of the building array becomes more 3.3. Overallrise-up uneven. In this section, we consider the effects of increasing the height by 1/3 H, 2/3 H, and 1 H, as shown in 3.4. Rise-up central area Figures 17,18,19. As observed from Figures 17 (rise-up 1/3 H), 18 (rise-up As shown in Section Overall rise-up, the overall rise-up 2/3 H), and 19 (rise-up 1 H), compared with Figure 12 (no did not improve the natural VP of the rear buildings. overhead), VP of the first row increased by 18%, 29%, and Therefore, in this section, we attempt to overpass the Figure 17. Rise-up 1/3H. 12 Z. YAWEN ET AL. Figure 18. Overall rise-up 2/3H. Figure 19. Overall rise-up 1H. central area of the building group. Additionally, we of the VPa/VPsd ratio decreased by 8%. Similarly, VPa consider the intermediate 3 × 3 rise-up area with 1 H increased by 31% and 24% for 25° and 45° wind direc- and intermediate 5 × 5 rise-up area with 1 H. tions, respectively, and the inhomogeneous VPa/VPsd Comparing the information shown in Figure 12 (nor- decreased by 20% and 16%, respectively. mal alignment; no overhead) and Figure 20 (3 × 3 area in Similarly, as shown in Figure 21 (5 × 5 area in the the middle of the overhead), VPa of the building com- middle of the overhead), VPa increased by 55%, 53%, plex increases by 28% at 0° wind direction. VP of the and 36% for the three wind directions of 0°, 22.5°, and three buildings surrounded by the orange line is 7 times 45°, respectively. The nonuniformity (VPa/VPsd) that of the unsettled building. However, the VP decreased by 15%, 29%, and 22%, respectively. increased by 63% for the three buildings enclosed by It can be observed that the overhead intermediate the light blue line. Simultaneously, VP in the middle 3 × area can significantly improve the natural VP of the 3 region increased by 242%. Second, the inhomogeneity building array, and the effect of the 5 × 5 overhead JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 13 Figure 20. Central 3 × 3 rise-up in a 7 × 7 array. center area is similar to that of the 3 × 3 overhead surrounded by the green line is five times the VP of center area. the unraised buildings. However, VP of the unraised buildings in the middle increased by 140%. Relative to the normal arrangement (no rise-up) 3.5. Rise-up peripheral area shown in Figure 12, VP of the building shown in In this section, we analyze the effect of the peripheral Figure 23 increased by 53% when the wind direction building overhead, including raising the outer two was 0°. VP of the seven buildings enclosed by the brown circles of the buildings (Figure 22) or raising the outer line is 1.4 times that of the unraised buildings. VP of the 1 circle of buildings (Figure 23). five buildings enclosed by the green line is 4.9 times that Compared with the information shown in Figure 12 of the non-overhead building. However, VP of the (normal alignment; no rise-up), VPa of the building unraised buildings in the middle increased by 78%. increases by an average of 28% for the 0° wind direc- Compared with the rise-up middle area, VP of the tion in Figure 22. Among them, VP of the seven build- model shown in Figure 22 increased by 20%, and that shown in Figure 23 increased by 7%. The cases at 22.5° ings enclosed by the brown line is 1.4 times that of the and 45° were similar. unraised building. VP of the three buildings Figure 21. Central 5 × 5 rise-up in a 7 × 7 array. 14 Z. YAWEN ET AL. Figure 22. Peripheral 2 row rise-up by 1H in 7 × 7 array. Figure 23. Peripheral 1 row rise-up by 1H in 7 × 7 array. It can be observed that compared with the rise-up As shown in Figure 24, compared to Figure 12 (nor- central area (Section Rise-up central area), VP of the mal alignment; no rise-up), VPa of the building com- peripheral buildings increased and that of the central plex increased by 95% for the three wind directions. buildings decreased, i.e., the non-uniformity increased. Simultaneously, VP inhomogeneity (VPa/VPsd) decreased by an average of 36%. It can be observed that the staggered rise-up can significantly increase the VP and uniformity. 3.6. Staggered rise-up Based on the conclusions mentioned in Sections Rise-up central area and Rise-up peripheral area, we realized that 3.7. Annual frequency of wind direction building overhead does improve natural ventilation, but the non-uniformity of VP is very obvious. Therefore, in The results obtained in the previous sections show that this section, we propose a new staggered rise-up wind direction can significantly affect the VP distribution arrangement to study its effect on building arrays. of a building array. In this section, we considered the JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 15 Figure 24. Staggered rise-up. frequency of wind distribution in three cities in China to (a) normal and (b) staggering 0.5 H is small, with the observe the magnitude of the VP throughout the year. former being 20% more inhomogeneous than the latter. Three cities and their corresponding climate zones (c) The VP of staggering 1 H was 20% higher than that of were selected: Beijing (cold climate zone), Changsha the previous two. The potentials of (d) rise-up 1/3 H, (e) (hot summer and cold winter climate zone), and rise-up 2/3 H, and (f) rise-up 1 H increase in turn with Guangzhou (hot summer and warm winter climate similar inhomogeneities. VPa of (g) central 3 × 3 rise-up zones). Wind direction data were obtained from the and (h) central 5 × 5 rise-up increased while the inhomo- Chinese Meteorological Dataset. (Department of geneity decreased. The average potential of (i) peripheral Building Technology and Science, Tsinghua University two row rise-up and (j) peripheral one row rise-up 2005) A temperature suitable for natural ventilation (15– increased, but the heterogeneity also increased. (k) The 30 °C) was selected from a typical meteorological year, staggered rise-up had the largest increase in VP and the and the number of hours in the 16 wind directions was lowest inhomogeneity. counted in the typical year. A wind rose diagram (fre- In summary, rise-up central and staggered rise-up quency of wind direction) was drawn, as shown in are the best for wind VP. Figure 25, and VP throughout the year for different arrangements is shown in Figure 26a. Note that because 3.8. Correlation between air age and potential of the similarity in airflow motion between buildings distribution (Reynolds independence), the wind speed was not considered. Air age was first proposed by Sandberg et al. As shown in Figure 26b, although the wind direction (Sandberg and Sjöberg 1983) in the 1980s and is frequencies in the three cities were significantly different, defined as the time it takes for air to reach the VPs were almost the same. Hence, only Beijing is a certain position in the space from the inlet. This discussed below. The difference in the mean VP between can reflect the freshness of indoor air. The smaller Figure 25. Wind-direction frequency in Beijing, Changsha, and Guangzhou. 16 Z. YAWEN ET AL. Figure 26. Annual VP of Beijing, Changsha, and Guangzhou under different arrangements. a JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 17 Figure 26. (Continued). 18 Z. YAWEN ET AL. the air age, fresher the air and better the air quality. The tensor expression of the air age transport equa- tion under steady-state conditions (Zhu 2016) is given using Equation (9). � � @ @ @ ðρu � τÞ ¼ Γ � þ ρ; (9) i A @ x @ x @ x i i i where τ is the air age at a certain point in the room; ρ is the air density; u is the velocity vector; Γ is the diffusion coefficient of the air age; μ μ Γ ¼ þ ; μ is the aerodynamic viscosity; μ is S Sc c t the air turbulent viscosity; S and Sc are the c t Schmidt and turbulent Schmidt numbers, respec- tively. In this study, inlet of the flow field was used as the starting surface for calculation, and the above mentioned control equation of air age was compiled into a UDF(User defined function) to obtain the air age distribution. Figure 27. Locations of air age for Building a. To compare the correlation between air age dis- tribution and the distribution of natural VP, the simulation results of a 7 × 7 normal arrangement can be obtained by comparing with the ventilation and staggered overhead arrays matrix at three potential distribution in Figure 24. wind direction angles were used for comparison. Compared with the staggered overhead in The air age at a distance of 0.5 H m from the Figures 28 and 29, in the three wind directions, the ground, which is at the horizontal plane of 0.0075 average air age of the buildings with the staggered m. Based on the fact that windows can be opened overhead is smaller than that of the buildings with the all around the building, in order to assess the fresh- positive arrangement. As mentioned in Section ness of air around the building, the air-age value of Staggered rise-up, the average ventilation potential a single building was obtained by averaging the air- of staggered overhead buildings is higher than that age values of one point at each of the four azi- of positive arrangement, which can also confirm that muths of the building at 0.5 H. The value points the distribution of air age and building ventilation are shown in Figure 27, and the calculation formula potential is in an opposite trend. Furthermore, after is given in Equation (10). The formula for calculating considering the distribution frequency of the wind the standard deviation of air age is shown in direction throughout the year, the annual distribution Equation (11). The comparison between the distri- of air age in the three typical climate regions of Beijing, bution of natural VP and distribution of air age is Changsha, and Guangzhou is shown in Figure 30. shown in Figures 28 and 29. Compared with the information shown in Figure 26a, b (VP distribution), it can be observed that the distribu- t1þ t2þ t3þ t4 t ¼ (10) tion of natural ventilation potential has an opposite trend to the distribution of air age. In the dominant wind direction suitable for natural ventilation, the sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 building on the windward side demonstrated smaller t ¼ ðti tÞ ðsÞ (11) n air age and better air quality. However, this advantage i¼1 is not evident. where n is the number of squares in the computational array; t is the air age value of the ith square, s; and t indicates the average air age value of all squares. 4. Discussion As shown in Figure 28, in contrast to Figure 12 In this study, we discuss the effects of density, stagger- (normal alignment) for VP distribution, the distribution ing, and lifting on natural ventilation using the stan- of the air age is opposite to the distribution of VP for dard deviation of wind pressure on building facades in the three wind directions. On the windward side, the ideal building arrays. The annual wind direction and air age was small, VP was high, and air quality was wind distribution frequency were also considered. excellent. Conversely, downstream buildings had It can be seen from sections Influence of building older air quality, low VP, and poor air quality. For density, Staggered VS normal arrangement, Overall rise- a more special building arrangement with staggered up, Rise-up central area, Rise-up peripheral area, and overhead as shown in Figure 29, the same conclusion JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 19 Figure 28. Distribution of air age in a 7 × 7 building array. Figure 29. Distribution of air age in a Staggered rise-up building array. 20 Z. YAWEN ET AL. Figure 30. Annual distribution of air age in Beijing, Changsha, and Guangzhou climate zones. Staggered rise-up that the incoming wind direction has of a building array. In this study, we found that the a significant impact on the overall ventilation potential staggered layout only improved the ventilation in the first 2 or 3 rows of buildings, and the improvement of of the building complex. However, if the frequency of the overall ventilation effect of the building group is not wind direction throughout the year is considered, the obvious. It can be observed that the staggered arrange- effect of wind direction is not obvious, as shown in ment has limitations in terms of the improvement effect Section Annual frequency of wind direction. This actu- of the wind environment. Hanna et al. (2002) observed ally means that the orientation of the building complex that the air velocity between buildings was nearly con- has not much effect on the ventilation potential. stant after the third row of an 8-row building array. In It can be found that the partial overhead (sections Zaki, Hagishima, and Tanimoto (2012), it is mentioned Rise-up central area and Rise-up peripheral area) and that the pressure coefficients for a building density λ the staggered overhead (section Staggered rise-up) of 7.7% are diamond, square, and staggered, and the have better ventilation effects. The reason is that the pressure coefficients for a λ of 30.9%-39.1% are very distance between the buildings is widened and the similar regardless of the building arrangement. In this density is reduced. This once again proves that density study, a λ of 25% was used, and the difference in ventila- is the main factor affecting the ventilation potential of tion potential was found to be small when comparing a building complex. square and staggered arrangements. Building density Similar to the experimental results obtained by Kim, does have an effect on the ventilation potential for dif- Yoshida, and Tamura (2012) and Buccolieri et al. (2019) ferent building arrangements, and this paper is limited by in a wind tunnel, we observed that the building density considering only a building density of 25%. We will cor- has a strong effect on the natural VP. The greater the rect and further investigate this in our subsequent building density, more uneven the VP distribution. studies. Asfour (2010) observed that when a building faces Tsutsumi, Katayama, and Nishida (1992) observed the dominant wind direction of the region, the VP of that in an array with more than 10 rows, the wind the building on the windward side is better, which is pressure coefficient reached a stable value in the fifth similar to the results obtained in this study. 2018) also row when the array was positive. However, when stag- suggested that the mainstream wind direction should gered, stable values were achieved in the seventh row. be considered in a building row. However, in this The standard deviation of wind pressure obtained in study, we observed that the influence of mainstream this study was almost unchanged after 2–3 rows. It can wind direction became very small after considering the be seen that in a building array, after a certain number annual wind direction data. of rows, the wind speed and pressure do not change Zhang, Gao, and Zhang (2005) observed that significantly. a staggered layout could improve the ventilation effect JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 21 Overall, in this study, we observed that building stage. In the next study, the height and porosity of density has a significant impact on the natural ventila- buildings will be discussed. tion potential of the building itself. The advantage of staggered alignment over normal alignment is not Acknowledgements obvious when considering the frequency of wind direction throughout the year. A staggering rise-up The original idea for the study originated from can significantly increase VP and uniformity. The dis- a collaboration with IEA-Annex62 Ventilative Cooling and IEA-Annex 80 Resilient Cooling. tribution of natural ventilation potential based on the wind pressure standards of the building facade shows an opposite trend to the air age distribution. But the Disclosure statement method in the study are focusing on air change ability of each building in a layout, and the air age reflects the No potential conflict of interest was reported by the freshness of the outdoor air. They are related but have authors. completely different meanings. This study has several limitations. The shape of the Funding building was too simplified (all considered as cubes), and factors, such as thermal buoyancy, pollutants, and This study was funded by the National Natural Science terrain, were not considered. Also, many other arrays Foundation of China (No. 51308206), International Science are not discussed. and Technology Cooperation Program of China (No. 2014DFA72190), Natural Science Foundation of Hunan Province of China (No. 2021JJ30269), Scientific Research Foundation of Hunan Education Department (No. 20B217) 5. Conclusion The effects of different wind direction angles, dif- Notes on contributors ferent building densities, staggering, overall over- head, partial overhead, and wind direction Zhong Yawen currently is a master student in building frequency on the natural VP in the building array envrionment and energy. She is now focusing on building natural ventilation and CFD simulation. were simulated using CFD, and the conclusions are summarized as follows: Yin Wei serves as an associate professor of building envrion- ment and energy. His main research areas include natural ventilation, building wind environment, underground venti- (1) An evaluation method based on the standard lation, subway station ventilation, kitchen ventilation, and deviation of wind pressure on the surface of the ward ventilation in hospitals. On the other hand, he is cur- building can be used to evaluate the natural rently moving towards passive houses and near-zero energy ventilation effect of the building group. It buildings. reveals the ventilation capacity of each building Li Yonghan is a master student focusing on ventilative cool- and is related to the air age, but its meaning is ing. Hao Xiaoli Hao Xiaoli is a full professor in building completely different. The air age reflects the envrionment and energy, who studies passive cooling tech- freshness of the air. niques in buildings. (2) The greater the density of the building group, Zhang Shaobo is a PhD in cooling technology. smaller the potential, and this relationship Han Qiaoyun is a PhD in ventilation of underground spaces. behaves like an exponential function. Duan Shuangping is an associate professor focusing on ven- (3) In a single wind direction, a staggered arrange- tilative cooling and solar energy. ment can improve VP. However, when consider- ing the frequency of wind direction throughout the year, the effect becomes weak. ORCID (4) Raising the central area can improve the VP of an entire building array. Yin Wei http://orcid.org/0000-0001-6632-804X Hao Xiaoli http://orcid.org/0000-0002-4532-870X (5) When the peripheral area is elevated, it will Han Qiaoyun http://orcid.org/0000-0003-0909-9185 increase VP inhomogeneity between peripheral and intermediate area. (6) The staggered rise-up has a significant improve- References ment effect on VP of the building group because Al-Sallal, K. A., M. M. 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Beijing, China: Multiplicity, Window Opening Percentage, Air Velocity China Architecture & Building Press.
Journal of Asian Architecture and Building Engineering
Taylor & Francis
Evaluation of building arrangement on natural ventilation potential in ideal building arrays
Journal of Asian Architecture and Building Engineering
, Volume 22 (6): 23 –
Nov 2, 2023
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