Cfd Simulation of Micro-climate in Waterfront

As an important influence factor of unban climate, unban water bodies take positive effect on the microclimate of surrounding area in summer. This paper firstly evaluated performances of five different models comparing with experimental results of windtunnel to find out which is most suitable for thermal prediction of micro-climate. Then, SKE model was selected to simulate four planning cases considering solar radiation and humidity transportation to predict wind and thermal-humidity environment. The results mainly discussed thermalhumidity distribution in full scale, finding the relevance between building distribution and moisure difussion on micro-climate around water body. INTRODUCTION The urban air temperatures of most large cities have increased gradually during the summer in recent years. It is understandable that the concern regarding on the methods of improving quality of urban thermal environment has increased dramatically, especially interested in natural underlying surface such as water body. Many studies have conducted on the interactions between water body and atmosphere. For instance, Badtinath et al. (2004) studied the effects of small water bodies on transportation of heat and water vapor between the water and atmosphere. However, most studies were limited to the viewpoint that atmospheric conditions was an important factor that influence thermal properties within rivers, they were not directly interested in the impact of river on surrounding climate. In recent years, due to various considerations of unban thermal environment, complex types of underlying surface and largescale urban construction, the numerical simulation methods take advantage of its flow analysis on optimal design comparing to other solutions(Akashi et al. 2008). Many reasearchers in china have used CFD to predict thermal envrionment by different purposes (Zhao et al. 2002), (Chen et al. 2007), however, few were involved in the relationships among water bodies, building distribution of waterfront and unban thermal climate. Fewer experiments, field and numerical studies have performed for this thermal effect because of many difficulties. Ken-ichi NARITA (1992) has conducted windtunnel experiment to model the thermal effect of water body on airflow around buildings. Hathway et al. (2012) took small area of urban river as object to investigate the “cooling power” on thermal climate and the results showed power of the cooling was related with wind speed, river’s temperature and solar radiation. Besides, Ashie et al. (2006) used CFD to research the phenomenon of temperature increase in downwind area, finding the architectural layout was an important factor on reducing urban heat island. This paper firstly evaluated the performance of velocity and thermal-humidity distribution by five different modeling approaches comparing with the results of windtunnel tests. Then, the SKE model was selected to predict thermal-humidity environment considering solar radiation and humidity transportation. The purpose of this study is to investigate important influence factors on moisure difusion in micro-climate around water body. PERFORMANCE EVALUATION OF VARIOUS TURBULENCE MODELS Wind tunnel model The main purpose of this research is to discuss regularities of thermal envrioment distribution with differernt distric layouts. In order to get more accurate result of heat and moisture diffusion, firstly several turbulence models were discussed based on windtunnel test (Ken-ichi NARITA. 1992) to meet the simulation accuracy. The outline of the windtunnel workstation is shown as Figure 1. The size of cubic block was 0.03 metre. Building coverage rate was about 56%; the water body model for width was 0.2 m, 2 m long. Consider the embankment effect, the height between water and ground was about 0.005 m. the wind direction, water body and the relative position of the buildings were shown in Figure 2. The measuring points’ height from the ground was 0.01 m, 1.75 m away from starting reference point, which were illustrated as samplingsamplingsampling area in Figure 1. The reference point was shown in Figure 1 at the beginning of water channel. Wind speed was exponential distribution with index coefficient of 0.25. Building boundary layer thickness was about Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, August 26-28