Seite - 88 - in Intelligent Environments 2019 - Workshop Proceedings of the 15th International Conference on Intelligent Environments

0.1= k σ , 3.1= ε σ , 44.1 1 = ε C , 3.1 2 = ε C , 13 εε CC = )0( ≥ k G , 0 3 = ε C ; )0( < k G ; 1εp C , Correction coefficient for the resistance coefficient of tree inε equation (=1.8). Acknowledgement The UK Engineering and Physical Sciences Research Council Doctoral Training Grant and the Japanese Society for the Promotion of Science funded this research. Thanks go to Kanda Laboratory for sharing the data about COSMO and to all of those who allowed to start up the collaboration between the Building Research Institute in Tsukuba science city in Japan and the Bartlett School at University College London. References [1] Ashie, Y., Kono, T., 2011. Urban-scale CFD analysis in support of a climate-sensitive design for the Tokyo Bay area. Int. J. Climatol. 31, 174–188. [2] Bakkali M., Inagaki A., Ashie Y., Yoshida Y., Kanda M., Raasch S., 2015. Thermal large eddy simulation with sensible heat flux distribution from various 3d building geometries, Journal of Japan Society of Civil Engineers, vol.71, issue 4, pp.I_433-I_438 [3] Bouyer, J., Inard, C. & Musy, M., 2011. Microclimatic coupling as a solution to improve building energy simulation in an urban context. Energy and Buildings, 43(7), pp.1549–1559. [4] Gagge, A.P., Fobelets, A.P., Berglund, L.G., 1986. A Standard Predictive Index of Human Response to the Thermal Environment. ASHRAE Trans U. S. 92:2B. [5] Hirt, C.W., 1993. Volume-fraction techniques: powerful tools for wind engineering. J. Wind Eng. Ind. Aerodyn. 46–47, 327–338. [6] Huang, H., Ooka, R. & Kato, S., 2005. Urban thermal environment measurements and numerical simulation for an actual complex urban area covering a large district heating and cooling system in summer. Atmospheric Environment, 39(34), pp.6362–6375. [7] Kikegawa, Y. et al., 2003. Development of a numerical simulation system toward comprehensive assessments of urban warming countermeasures including their impacts upon the urban buildings’ energy- demands. Applied Energy, 76(4), pp.449–466. [8] Mochida, A., Tabata, Y., Iwata, T., Yoshino, H., 2008. Examining tree canopy models for CFD prediction of wind environment at pedestrian level. J. Wind Eng. Ind. Aerodyn. 96, 1667–1677. [9] Mochida, A. et al., 2006. Total analysis of cooling effects of cross-ventilation affected by microclimate around a building. Solar Energy, 80(4), pp.371–382. [10] Salamanca, F. et al., 2010. A new building energy model coupled with an urban canopy parameterization for urban climate simulations—part I. formulation, verification, and sensitivity analysis of the model. Theoretical and Applied Climatology, 99(3-4), pp.331–344. [11] Salamanca, F. & Martilli, A., 2010. A new Building Energy Model coupled with an Urban Canopy Parameterization for urban climate simulations—part II. Validation with one dimension off-line simulations. Theoretical and Applied Climatology, 99(3-4), pp.345–356. [12] Stüben, K., 2001. A review of algebraic multigrid. J. Comput. Appl. Math. 128, 281–309. [13] Tanimoto, J., Hagishima, A. & Chimklai, P., 2004. An approach for coupled simulation of building thermal effects and urban climatology. Energy and Buildings, 36(8), pp.781–793. [14] Yaghoobian, N. & Kleissl, J., 2012. An indoor–outdoor building energy simulator to study urban modification effects on building energy use – Model description and validation. Energy and Buildings, 54, pp.407–417. [15] Zhai, Z. et al., 2002. On approaches to couple energy simulation and computational fluid dynamics programs. Building and Environment, 37(8–9), pp.857–864. M.Bakkali andY.Ashie /RANSModelling forLocalClimates,EnergyUseandComfortPredictions88