Reliability verification of an assessment tool for outdoor thermal environment

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1 IOP Conference Series: Materials Science and Engineering Reliability verification of an assessment tool for outdoor thermal environment To cite this article: Y S Jee et al 2010 IOP Conf. Ser.: Mater. Sci. Eng View the article online for updates and enhancements. Related content - Prediction and evaluation method of wind environment in the early design stage using BIM-based CFD simulation Sumi Lee and Doosam Song - Enhanced finite element scheme for vibrational and flow induced sound M Kaltenbacher, S Triebenbacher, B Wohlmuth et al. - Convective heat transfer in airflow through a duct with wall thermal radiation T T Chandratilleke, R Narayanaswamy and P Wangdhamkoom Recent citations - A review on the CFD analysis of urban microclimate Y. Toparlar et al This content was downloaded from IP address on 03/09/2018 at 18:27

2 Reliability Verification of an Assessment Tool for Outdoor Thermal Environment Y S Jee 1, J Y Lim 2 and D S Song 3 1 Graduate School, Sungkyunkwan University, Suwon, Korea 2 Graduate School, Sungkyunkwan University, Suwon, Korea 3 Departtment of Architecture Eng., Sungkyunkwan University, Suwon, Korea 1 itsmnie@skku.edu, 2 reonheart@skku.edu, 3 dssong@skku.edu Abstract. In case of simulating the outdoor thermal environment, the radiation calculation is very important. In this study, as a new method to evaluate the outdoor thermal environment precisely, a coupled simulation of convection and radiation is proposed. And the suggested simulation method is validated by comparison of simulation results with those of field measurement. 1. Introduction As interest in the quality of life is increasing, interest in the outdoor thermal environment is also increasing. However, the rising height and density of buildings due to the rapid urbanization, industrialization, and population concentration in cities has decreased greens and water surfaces in the downtown area. Furthermore, due to the construction of infrastructure facilities and buildings, heat generated from the downtown area is not released which leads to heat island phenomenon in cities and microclimate of cities is gradually getting worse. Accordingly, interest in the control of microclimate in large cities as well as research on outdoor thermal environment is increasing. In this context, the accurate prediction and evaluation of outdoor physical environment is very important in order to improve outdoor thermal environment and guarantee the comfort of residents. In Korea, many environmentally-friendly architectural techniques such as the arrangement of greens or water spaces and wind road in urban spaces are being tried to improve the outdoor thermal environment, and many studies on this subject are being conducted. However, these studies mostly focus on investigating individual physical phenomena through measurements such as the cooling effect of a single species of tree [1] or solar heat reduction materials [2], and temperature changes by land use conditions [3], and few of them generally evaluate and predict the physical effects of environmentally-friendly architectural techniques on the downtown area. In other countries, many studies have been conducted on this subject. Asawa et al. [4] developed an analysis program for outdoor thermal environment that can calculate view factor and analyze radiant heat transfer which is used for analysis of outdoor thermal environment which contains builsinf structures and trees. Yoshida et al. [5] analyzed the effects of the ratio of greenness on outdoor space through coupled simulations of convection, radiation, humidity. Mochida et al. [6] predicted the wind environment of the pedestrian level through CFD (Computational Fluid Dynamics) and presented a technique for suggesting optimum tree arrangement considering outdoor thermal environment. Chen et c 2010 Published under licence by Ltd 1

3 al. [7] studied on the optimum design method for outdoor space using CFD simulation and the Genetic Algorithm technique. The effect of radiation is dominant in outdoor space, but existing commercial code for CFD simulation cannot be treated the radiation heat exchange with accuracy, and there is a difference between the simulation results and those of field measurement. Thus, preceding researches to predict the outdoor thermal environment with numerical simulation methods were accomplished by in house radiation heat exchange calculation method. In this study, the coupled simulation of convection and radiation analysis method which adopt the Monte Carlo method for view factor calculation and Gebhart absorption method for radiation calculation and STAR CCM+ for CFD simulation was suggested[8][9]. This paper describes the coupled simulation of convection and radiation analysis technique outline developed by this study, Also the thermal environment analysis of an actual space was performed by field measurement, and the reliability of the coupled simulation of convection and radiation will be verified by comparing actual measurements results and those coupled simulation. 2. Outline of the Coupled Simulation of Convection and Radiation Figure 1. Outline of Outdoor Thermal Environment Evaluation Techniques Figure 1 shows the outline of the coupled simulation of convection and radiation for outdoor thermal environment analysis. As shown in Figure 1, the boundary conditions are set for calculation of radiant heat exchange of the outdoor environment. The earth surface and wall surface temperatures are calculated as the results of the radiation calculation and these results are used as the new boundary conditions for the convective heat exchange humidity calculation. The radiation calculation is performed again on the basis of the CFD calculation results. Through this process, the physical elements of the outdoor environment such as air velocity, temperature, radiation temperature, humidity, and others are determined more accurately. 2

4 2.1 Calculation of Radiant Heat Exchange The temperatures of earth surface and the external surface of buildings are very important physical quantities when evaluating outdoor thermal environment through simulation. Thus, the analysis of the heat and humidity transfer of the earth surface or the external wall surface of buildings is an essential part in the analysis of outdoor thermal environment. Figure 2 shows heat flux between earth surface and around structures. The calculation of radiant heat exchange in the proposed analysis technique for outdoor thermal environment is the process of defining the surface temperature T i through the heat flux equation defined in Equation (1) for each mesh of the analysis area. Figure 2. Heat Flux Model for Earth Surface and Building Surface Where, (1) S i : Short wave solar radiation for surface element i [W] R i : Long-wave solar radiation [W] H i : Convective heat transfer [W] C i : Heat flux to building and earth [W/m 2 ] LE i : Latent heat release by the evaporation of plants [W] To calculate the radiation field, the heat transfer between the surfaces must be accurately predicted. For this purpose, it is important to derive the view factor that determines the mutual positional relationships between the radiation surfaces. The proposed analysis technique calculated the view factor using the Monte Carlo method. According to the Monte Carlo method, if N ij is the ratio of energy that reaches the surface element j in the total energy (N itotal ) released from each surface element i comprising the space, the view factor in Equation (2) F ij is defined as follows: (2) Furthermore, the energy released from each surface element is reflected from any surface except the releasing surface and absorbed to the surface element including the sky. In this case, the solar energy reflected to the sky is released to the space, the solar shortwave energy S Aij released from the surface element i and absorbed to the surface element j can be calculated by the following Equation (3) using the Gebhard absorption B ij coefficient: Where, S Ri : Radiance reflected from the surface element i [W/m 2 ] (3) 3 Analysis of Outdoor Thermal Environment using the Proposed Analysis Technique This chapter shows the actual measurements of the analysis areas and describes the analysis results for outdoor thermal environment using the proposed coupled simulation of convection and radiation. Then, the reliability of the proposed technique will be verified by comparison of the measurements and the simulation results. 3

5 3.1 Analysis Areas Figure 3. Measurement and Calculation Areas Measurements were performed at center (area B) of the multi-residential housing block in Suwon, Korea (see Figure 3) for four days from August 4 to 7, Summarizing the average weather of Suwon in August in the past, the average temperature was 25.2 (lowest: 21.6, highest: 29.2 ), the average wind speed was 1.4m/s (35m above ground), and the average relative humidity was 79.8%. During the measurement period of this study, the average temperature was 37.8 (lowest: 22.3, highest: 37.6 ), the average wind speed was 0.22m/s (highest: 6.1m/s, 3m above ground), and the average relative humidity was 66.5%. These were all things which measured at rooftop of area C in multi-housing block. 3.2 Measurement outline The measured spots, measured items, measurement tools and locations are shown in Table 1. To measure the thermal environment of target area, measurement tools such as a pyrometer, a hot wire anemometer, and a glove thermometer is installed at the center of the area (B in Figure 3), and a weather station (3m high) outside the pergola (C in Figure 3) which could measure the wind speed, wind direction, temperature, and humidity. Considering that wind speed is a dominant factor for thermal comfort in outdoor environment during summer, wind speed was measured in intervals of 10 seconds and other values were measured in intervals of 1 minute. Considering that the averaging interval of the measurements greatly affect the changing patterns of data because radiance in outdoor space varies frequently even during a short period, all the data were averaged and analyzed in intervals of 5 minutes. 4

6 Measured Spot A B C Table 1. Measured Spots, Items and Tools Measured Item Measured Tool Manufacturer Model Name Surface/Skin Temp. Thermal Imaging Camera NEC TH7800 Velocity, Temp. Hot wire Anemometer KANOMAX Model 6651 Long/Shortwave Radiation, Pyrometer* Net Radiation, Albedo Kipp & zonen CNR1 Globe Thermometer Glove Temp. (Globe, K-type Thermocouple, Data Logger) YOKOGAWA MX100 Wind speed/direction Wind speed/direction Sensors* R.M YOUNG Temp./Humidity Temp./Humidity Measuring Sensors* Campbell Scientific HMP45C * Data Logger (CR3000, Campbell Scientific) collected. Interval : 10 sec Interval : 1 min Note - Height 1.5m Height 3m 3.3 Simulation outline To analyze the thermal environment for analysis area B, the area B cannot be just analyzed, because the speed, direction and temperature of the wind that flows into the area B are important variables. Thus, to determine the characteristics of the wind that flows into the area B, the air flow is analyzed by using CFD simulation for the wide area (area A) that surrounds the area B. At the same time, the radiation field in area B is analyzed and derived the boundary condition of surface temperature using a separate radiation heat flux analysis program. These characteristics of the wind flowing into area B were input as the boundary conditions for the CFD simulation for thermal environment analysis, and the space distributions of wind speed, temperature, humidity, and radiation temperature of the analysis area were determined[10][11]. The radiant heat flux based on radiant heat transfer was calculated for area B surrounded by a solid line in Figure 3, and the temperatures of the earth surface and building surface were calculated. Then, to determine the wind speed, temperature, etc. in each mesh, convection calculation based on the standard k- model was performed using the temperatures of the earth surface and building surface of area B which was determined by radiation calculation as the boundary conditions. Figure 4 (a) shows the mesh for radiation calculation. The size of each mesh is 4m x 4m. Smaller mesh size improves analysis accuracy but exponentially increases the calculation time. So the appropriate mesh size was determined through trials and errors. For radiation analysis, even if the mesh size becomes somewhat large, the temperature for each surface element can exhibit a linear change through interpolation. The surface boundary conditions for radiation calculation were set as shown in Table 2 by referring to existing literature[12]. For other conditions for calculation, the heat resistance of the apartment wall was set to 0.99m 2 K/W, the room temperature of the apartment was set to 28 o C, and the total indoor heat transfer rate and the convective heat transfer rate of the walls interfacing with the external air were set to 9.3W/m 2 and 9.9W/m 2, respectively. In addition, the thermal conductivity of the ground was set to 1.16W/mK, the ground temperature was set to 26 o C, and the convective heat transfer rate of the earth surface was set to 10.0W/m 2. 5

7 (a) Mesh Generation for Radiation Calculation (b) Mesh Generation for CFD Calculation Figure 4. Mesh Generation of Area B Table 2. Surface Boundary Conditions for Radiation Calculation Albedo[-] Emissivity [-] Evaporation efficiency [-] Green Zone Asphalt Concrete Concrete(Building) Block Convection calculation for area B was performed with the surface temperature distribution determined by radiation calculation and the measured characteristics of the inflowing wind as the boundary conditions. However, a finer mesh was created because it is difficult to accurately determine air current distribution using the mesh for radiation calculation (Figure 4 (a)). This analysis used uniform meshes with the size of 1m x 1m (Figure 4 (b)). 4 Verification of the Reliability of the coupled simulation by Comparison with Measurement Results 4.1 Flow fields Figure 5 shows the simulation results of horizontal distributions of the wind speed in the analysis area (1.5m, 3m). Figure 6 compares the analysis results with the wind speed measurements between 15:00 and 16:30 on August 6, at measurement spots D and E in the analysis area. The two values matched on the whole: measurement results were 0.84m/s and 0.35m/s on average at D and E, respectively, whereas the simulation results were 0.96m/s and 0.32m/s, respectively. 6

8 Area D (a) Velocity Vector Horizontal distribution (Height 1.5m) (b) Velocity Scalar Horizontal distribution (Height 1.5m) [m/s] Area E (c) Velocity Vector Horizontal distribution (Height 3m) (d) Velocity Scalar Horizontal distribution (Height 3m) [m/s] Figure 5. Wind Speed Distributions in Simulation Results (Area B) Velocity(m/s) Area D(Measured, Avg.=0.84 Area E(Measured, Avg.=0.35 Area D(Simulated, 0.96) Area E(Simulated, 0.32) Time(hh:mm) Figure 6. Air velocity Comparison between Measurements and Simulation Results (Area D (1.5m) and Area E (3m)) 7

9 4.2 Surface Temperature Distribution (a) Earth Surface Temp. [ o C] (b) Building Surface Temp. [ o C] Figure 7. Surface Temperature Distributions in Simulation Results Figure 7 shows the calculation results for surface temperature in area B and Table 3 compares them with the measurement results. According to the calculation results for temperature distribution, the sunny and shaded places were approximately 38 o C and 35 o C, respectively in the block covered space, and approximately 32 o C and 26 o C, respectively in the green space. The distribution range of the measurements tended to a little wider than that of the calculations by simulation, although they matched on the whole. Such difference in distribution range appears to be due to the difference in the physical properties of the materials (blocks/plants) that actually comprise the surface even if the simulation assumed the same covering materials for each surface element. In the future, if the computer performance and capacity permit it, the mesh size needs to be made a smaller for radiation calculation finer for more detailed definition of the surface characteristics. In the case of the building surface temperature distribution, the calculations for sunny and shaded places were approximately 35 o C and 31 o C, which corresponded well to the measurement results like the case of earth surface temperature distribution. Temperature stratification is occurring by shades of the trees in front of the buildings. Higher parts of the buildings have higher sky factors and higher solar heat transmission to the walls, resulting in higher temperatures than lower parts of the buildings. Table 3. Comparison between Calculation and Measurement Results for Surface Temperature Measured [ o C] Simulated [ o C] View Thermal View(measured) Sunny Pavement Block Shade

10 WCCM/APCOM 2010 Shade 26.1૫ ૫28 Sunny 34.2૫ ૫37 Shade Building Surface Sunny Green Space 32.5૫ ૫ ૫ ૫ Temperature Distribution (a) Temp. Horizontal Distribution (Height 1.5m)[oC] (b) Temp. Horizontal Distribution (Height 3m)[oC] Figure 8. Horizontal Distributions of Temperature in Simulation Results (Area B) Figure 9. Comparison between Measurements and Simulation Results (Area D (1.5m) and Area E (3m)) Figure 8 shows the horizontal distributions of temperature (1.5m, 3m) around the measurement area (area B). Figure 9 shows comparison of the calculations results with the measurement results in areas 9

11 D and E. The measurements were o C on average at D and o C on average at E which matched on the whole with the simulation results which were o C and o C, respectively. 5 Conclusions This paper outlined the proposed analysis technique for outdoor thermal environment and reported the results for thermal environment analysis for actual spaces. Comparison of the measurement results with the analysis results in actual outdoor spaces found that the outdoor thermal environment evaluation using the proposed analysis technique were reliable. In the future, the study will be improve the reliability and accuracy of the analysis technique through measurements and case analysis for a variety of objects. The outdoor thermal environment evaluation technique developed in this study enables the quantitative prediction for outdoor spaces in the design stage according to the building arrangement, tree arrangement, and earth surface coverings. Therefore, it can be used as a tool for downtown redevelopment, apartment complex design, and other applications. Acknowledgments This work was supported by grant No. R from World Class University (WCU) project of the Ministry of Education, Science & Technology (MEST) and the Korea Science and Engineering Foundation (KOSEF) through Sungkyunkwan University. References [1] D. H. Choi et al., 2006, Analysis of Passive Cooling Effect of the Tree by Field Observations in the Summer, Korean Solar Energy Society Collected Papers, Vol. 26, No.4. [2] D. H. Choi et al., 2007, Analysis of Passive Cooling Effect of Membrane Shading Structure and the Tree by Field Observations in the Summer, Korean Solar Energy Society Collected Papers, Vol. 27, No.4. [3] W. H. Hong et al., 2007, Characteristics of Urban Temperature and Thermal Environment Simulation According to Land Use, The Architectural Institute of Korea Collected Papers, Vol. 23, No.9. [4] T. Asawa, A. Hoyano, K. Nakaohkubo (2008). Thermal design tool for outdoor spaces based on heat balance simulation using a 3D-CAD system, Building and Environment, 43, [5] Yoshida et al., 2000, Influence of Green Area Ratio on outdoor thermal environment with coupled simulation of convection, radiation and moisture transport, J. Archit. Plann. Environ. Eng. AIJ. No.529, [6] Mochida et al, Optimization of Tree Canopy Model for CFD Prediction of Wind Environment at Pedestrian Level, Journal of Wind Engineering, Vol. 108, pp [7] Hong et al, Study on optimum design method for pleasant outdoor thermal environment using genetic algorithms (GA) and coupled simulation of convection, radiation and conduction, Building and environment, Vol. 43, No. 1, pp [8] J. Y. Lim et al., 2009, Study on Assessment of Outdoor Thermal Environment with coupled simulation of convection and radiation, The Society of Air Conditioning and Refrigerating Engineers of Korea [9] S., Yoshida, 2000, Prediction and optimum design of outdoor thermal environment with unsteady coupled simulation of convection, radiation and conduction, Docteral ddissertation, University of Tokyo (in japaness). [10] A. Pfeiffer, V. Dorer, A. Weber (2008). Modelling of cowl performance in building simulation tools using experimental data and computational fluid dynamics, Building and Environment, 43, [11] B. E. Launder and D. B. Spalding (1974). The numerical computation of turbulent flows, Computer Methods in Applied Mechanics and Engineering, 3, [12] S. B. Kim et al., 2006, Eco-Friendly City Planning: Urban Heat Islands, Mun-Un-dang. 10