CFD Methodology Development for Singapore Green Mark Building Application

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Abstract

In the recent decade, investigation on the total building performance has become increasingly important for the environmental modelling community. With the advance of integrated design and modelling tool and Building Information Modelling (BIM) development, it is now possible to simulate and predict the building energy efficiency, air quality & health assessment, risk analysis & mitigation scenario for our urban planning analysis; all seamlessly in a single urban digital platform. In order to achieve the national goal of at least 80% of the buildings in Singapore to be green by 2030, Singapore Government has introduced the new BCA Green Mark 2015 scheme for accelerating the green building agenda. During the recent third Green Building Masterplan announced in 2015, it was decided to engage building tenants and occupants more actively to drive energy consumption behavioural change and to address the well-being of the people. Following up from this Masterplan, it is important for both the stakeholders and agency to jointly develop Performance Driven and Scientific Based Simulation Methodology and Evaluation Parameters as a frame work to evaluate the building design based on Singapore's hot and humid climate and densely-built-up urban areas for the Green Mark 2015 Scheme. In this paper, we will present the methodology and perform a baseline case study for the natural ventilation performance with the typical Non-Residential Building (NRB) industrial building. This can be resulted in the comprehensive CFD Quality Check List for the Environmental Sustainable Design (ESD) consultant in order to maintain modelling result accuracy. Demonstration on Indoor Air Quality (IAQ) using Air Exchange Effectiveness (AEE) as performance indicator will also be illustrated.

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APA

Chiu, P. H., Raghavan, V. S. G., Poh, H. J., Tan, E., Gabriela, O., Wong, N. H., … Leong-Kok, S. M. (2017). CFD Methodology Development for Singapore Green Mark Building Application. In Procedia Engineering (Vol. 180, pp. 1596–1602). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2017.04.322

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