Page - 76 - in Intelligent Environments 2019 - Workshop Proceedings of the 15th International Conference on Intelligent Environments

RANS Modelling for Local Climates, Energy Use and Comfort Predictions in Cities Mohammed BAKKALI a,b,1 and Yasunobu ASHIE c a The Bartlett School, University College London, UK b The International University of Rabat, Morocco c Building Research Institute, Japan Abstract. The RANS model takes into account all modes of urban heat transfer at higher spatial and time resolution. It is based on dynamic coupling between heat balance and k-epsilon (turbulence) modelling. The effect of complex urban spatial data is considered. The model assesses environmental information such as micro- climates, building energy and thermal comfort at neighbourhood and city scales. This paper presents details of the model with some tests against measured data along with static versus dynamic coupling and some results in real urban settings. Keywords. RANS modelling, overheating, energy use, comfort, predictions, cities 1. Introduction In recent decades, meteorologists and engineers have been developing numerical models for temperature predictions so as to assess overheating impacts on health, thermal comfort [4] and building energy. Most of these models misrepresent the complexity of real physical processes. The development of new numerical methods has allowed such physical modelling to be undertaken [2]. Zhai et al. developed new approaches to couple energy simulation and computational fluid dynamics (CFD) geared towards better computational performance, thermo-physical urban parameterisation and iterative computing [15]. This type of modelling is not only orientated towards specific questions related to urban meteorology and building energy but it also aims to implement comprehensive assessment frameworks with regards to urban warming and climate-sensitive design [1] and [7]. Regarding this type of modelling, Yaghoobian and Kleissl developed an indoor-outdoor building energy simulator to study urban modification effects on building energy use [14]. Bouyer et al. developed a microclimatic coupling as a solution to improve building energy simulation in urban context [3]. Salamanca et al. developed a building energy model coupled with an urban canopy parameterisation for urban climate simulations [10] and [11]. In addition, Mochida et al., Huang et al. and Tanimoto et al. have developed models that consider interactions between indoor and outdoor building climates and related energy demand [9], [6] and [13]. The model presented here, aims to develop 1 Corresponding Author: The Bartlett, UCL Institute for Environmental Design and Engineering, Central House, 14 Upper Woburn Place, WC1H 0NN London, UK; E-mail: mohammed.bakkali.10@ucl.ac.uk. Intelligent Environments 2019 A. Muñoz et al. (Eds.) © 2019 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/AISE190026 76