evaluation of pedestrian space in summer season using cfd … › ~icuc7 › extended_abstracts ›...

Evaluation of pedestrian space in summer season using CFD analysis of outdoor thermal environment

coupled with multifractional human thermoregulation model Yukiko Kishi *, Shinji Yoshida *

*University of Fukui, Fukui, Japan Abstract In this paper, a field observation and a numerical analysis are outlined in order to investigate the inhomogeneous and unsteady thermal environment in outdoor space in summer season. The aims of the observation are to investigate effects of the transition of environmental conditions on pedestrian thermal comfort, and to collect meteorological data which are used for initial and boundary conditions on the numerical analysis. Spatial distributions of airflow, temperature, radiation, humidity, and thermal comfort index are calculated in the numerical analysis. By investigating results of field observation and the numerical analysis, it was confirmed that the high effectiveness of incorporating the distributions of environmental conditions around pedestrians. Key words: CFD analysis, Human thermoregulation model, Thermal comfort in outdoor space 1. INTRODUCTION Outdoor thermal environment in summer season is affected by unsteady and inhomogeneous factors, which include the physical environment (instantaneous changes and local distributions of environmental conditions, such as solar radiation, wind) and pedestrian’s activities (walking speed, rest time, and activity level). The authors have incorporated a multifractional human thermoregulation model into a simulation method based on CFD analysis on the outdoor thermal environment in order to include the effect of the inhomogeneous environmental conditions [1]. The next step of this study is to evaluate effects of the abovementioned method. Thus, in this paper, we describe the investigation on the inhomogeneous and unsteady thermal environment in the practical outdoor space in summer season using a field observation and the numerical analysis. 2. OUTLINE OF ANALYSIS 2.1. Study area The study area is set in the Bunkyo Campus of University of Fukui in the centre of Fukui city located on the north-west part in Japan. Figure 1 shows the campus layout. The study area is covered with several types of landuse such as paved road, car park, grass, and trees. 2.2. Outline of the field observation We carried out the field observation of the outdoor thermal environment in the campus in summer season (9 days in the period from August to September in 2007). The aims of this observation are to investigate effects of the transition of thermal environmental condition on pedestrian thermal comfort, and to collect meteorological data which are used for initial and boundary conditions on the numerical analysis. Meteorological conditions are measured on the rooftop of the building 1 of the Faculty of Engineering which is about 12m tall, as shown in Fig.1. A new mobile instrumentation cart developed by the author was used to investigate the changes in the thermal environment with the pedestrian’s activity. Fig.2 illustrates the image of the observation. The cart measures thermal conditions around 2 subjects at one-day observation by chasing the subjects. The tympanic membrane temperature, average skin temperature, metabolic rate,

*Corresponding author’s address: Yikiko Kishi, Faculty of Engineering, University of Fukui, 3-9-1 Fukui-city, Fukui, Japan: [email protected]

Fig.1 Plan of study area

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The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan

and evaporative heat loss of the subjects during the observation are also measured in this study. The walking route is illustrated within Fig.1. 2.3. Outline of the numerical analysis The target period is a clear sunny day in summer. Meteorological data measured at the campus on the period from September 2nd to 4th in 2007 are used in this study, since values of cloud cover during the observation on the period are relatively low among the observational days. The analysis starts from 6:00 on September 2nd, and a time integration of 56 hours is performed using the meteorological data. The thermal environment on the pedestrian space is evaluated using the results obtained at 14:00 on September 4th. The Meteorological conditions of air temperature, wind velocity, wind direction, relative humidity, sun’s azimuth, sun’s altitude, amount of global solar radiation, and amount of downward atmospheric radiation at 14:00 are respectively assumed to be 32.6°C, 3.6m/s, NW, 49%, 52.7°, 49.5°, 731W/m2, 547W/m2. In the present analysis, we have incorporated the multifractional human thermoregulation model ‘65NM’, developed by Tanabe et al. [2], into the simulation method based on CFD analysis. Details of numerical method are given in Yoshida et al. in 2000 [3] and Yoshida et al. in 2006 [4]. 3. RESUTS AND DISSCUSSIONS 3.1. Distributions of wind velocity vectors Figure 3 illustrates the horizontal distributions of wind velocity vectors at the height of 1.2m. Northwest winds blow from the side boundary into the computational domain, and separate at the northwest corner of the library building. Strong winds are also seen around the Integrated Research Building (IRB) which is the tallest among all buildings in the computational domain. 3.2. Distributions of ground surface temperature Figure 4 illustrates the distribution of ground surface temperature. The values at grass surface in sunny area are about 43 °C, and those in shade area are about 38 °C. On the other hand, the values at paved surface range from 50 to 60 °C in sunny area, and also range from 36 to 44 °C in shaded area. Thus, it has been found that the differences between the values at paved surface and those at grass surface in sunny area are quite large. 3.3. Evaluation on thermal comfort not including

effects of pedestrian’s activity In this section, we show the calculation results without including effects of pedestrian’s activity. This investigation approach has been applied in the existing evaluation on outdoor thermal environment by the authors [3, 4].

Fig.2 Image of field observation

Fig.3 Horizontal distributions of wind velocity vectors

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Fig.4 Distribution of ground surface temperature

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(1) Distributions of MRT Figure 5 illustrates the horizontal distribution of mean radiant temperature (MRT) for the whole human body. In this calculation, it is assumed that pedestrians face the sun, as shown in Fig.6 [note1]. The values on paved surface in sunny area are over 60 °C, which are quite high. Figure 6 compares the distribution of MRT on each body segment on sunny area (the point A in Fig.5) with that on shaded area (the point B in Fig.5). The difference between the values on Point A and that on Point B is quite large. On Point A, the value on the back is about 38 °C, in contrast, the value on the chest is about 58 °C. The difference between the back and the chest is about 20 °C, quite large. These differences are caused by existence or nonexistence of solar radiation on each body segment. (2) Distributions of new standard effective temperature (SET*) Figure 7 illustrates the horizontal distribution of new standard effective temperature (SET*) at the height of 1.2m [note2]. The values on paved surface in sunny area are about 42°C, and those in shaded area are also about 35°C. The values in grass area under the shade of trees also range from 30 to 35°C. The value in Point B is about 34.6°C, and is 7.8°C lower than that in Point A. Thereby Point B is much more comfortable place than Point A. 3.4. Evaluation on thermal comfort including effects of

pedestrian’s activity This section shows the evaluation results including effects of pedestrian’s activity as a example of the application of the method proposed by the authors [1]. The following results are obtained by changing the environmental condition for the multifractional human thermoregulation model in accordance with a pedestrian’s walking in the mobile field measurement. Two different walking routs are set in this analysis. In case1, a pedestrian rests at the sunny area (Point A) while at the shaded area (Point B) in case2. These walking routs are shown in Fig.1. By comparing between these calculation results, we attempt to investigate effects of thermal environment in rest space on the pedestrian’s thermal comfort. (1) Incident solar radiation from each direction Figure 8 shows the time variation of incident solar radiation from each direction to the pedestrian. This is one of the input environmental condition to the thermoregulation model. The value in Point A is about 388W. On the other hand, the value in Point B is about 74W, and is 314W lower than that in Point A. The disposition and direction of pedestrian also affect the value from each direction. As an example, the largest value in the Point A from the front direction is about 146W while the smallest value from the bottom direction is about 16W. This difference is about 130W, so large. (2) Mean skin temperature Figure 9 shows the time variation of mean skin temperature. The value in case1 at the end of rest time is about 35.3∘C, that in case2 is about 33.9∘C. The value in case1 is 1.4∘C higher than that in case2. This difference affects the calculation results till the end of calculation. Hence, it has been found that the difference of thermal environment in rest space strongly affects the thermal comfort for a pedestrian over a prolonged period of time.

Fig.5 Horizontal distribution of MRT


Fig.6 Distribution of MRT on each body segment

Fig.7 Horizontal distribution of SET*


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(3) Core temperature The time variation of core temperature is illustrated in Fig.10. Values in both cases gradually increase with walking time. The value in case1 at the end of rest time is about 36.2∘C, and that in case2 is about 36.1∘C. The difference is about 0.1∘C, and is maintained till the end of calculation, like the result of mean skin temperature. (4) Skin wittedness Figure 11 illustrates the result on skin wittedness. The values at the end of the rest time in both cases are about 80%. After that, the value in case2 temporarily decreases to about 67% by the effect of resting in shaded area (Point B). (5) Cumulative sum of sweat Figure 12 shows the time variation of cumulative sums of sweat. Values represented by full lines represent the sum of unevaporated and evaporated sweats, and contributes the weight loss after the pedestrian’s activity. The value in case1 at the end of calculation reaches 130g, and that in case2 100g. It is well known that the human body sweats in order to release more heat to surroundings. If we can regard the sweating as the result of inner effort by the human body, it has been found that the exercise intensity in case1 is about 1.3 times stronger than the intensity in case2. 4. Conclusion (1) The revised outdoor CFD analysis coupled with a

multifractional human thermoregulation model is applied to the evaluation of outdoor thermal environment on field observation in summer season. The calculation method enables us to evaluate the inhomogeneous effect of solar and longwave radiations and airflow on the outdoor thermal environment.

(2) The effects of pedestrian’s activity on outdoor thermal comfort are investigated using the calculation method. From results such as solar radiation, mean skin temperature, skin wettedness, it is clarified that the direction and disposition of pedestrian, and the thermal environment on rest space significantly affect the transion of the outdoor theramal comfort.

[note1] MRT values in the present analysis include the effect of solar radiation. Values of albedo (reflectivity of solar radiation) on each body segment are set on the basis of clothing for an office worker, which is assumed to ware a white short-sleeved shirt and dark-blue trousers. [note2] In the calculation of SET*, the value of metabolic heat generation is assumed to be 1.5met which is the middle activity level between walking and resting. References [1] Yoshida, S., Oguro, M., 2009, Preprints of the 7th

International Conference on Urban Climate [2] Tanabe, S. et al., 2002, Energy and Buildings, 34, 637-646 [3] Yoshida, S., Murakami, S., Ooka, R.,et al., 2000, Abstracts of

papers presented at the 3rd International Symposium on Computational Wind Engineering, 27-30.

[4] Yoshida, S., Ooka, R., Moshica, A., et al., 2006, Preprints of the 6th International Conference on Urban Climate, 320-323.

（1）case1 (restplace: sunny area)

（2）case2 (rest place: shaded area)

Fig.8 Time variation of incident solar radiation from each direction

Fig.9 Time variation of mean skin temperature

Fig.10 Time variation of core temperature

Fig.11 Time variation of skin wettedness

Fig.12 Time variation of cumlative sweat

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evaluation of pedestrian space in summer season using cfd … › ~icuc7 › extended_abstracts ›...

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