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This paper optimizes opening positions on building facades to maximize the natural ventilation’s potential for ventilation and cooling purposes. The paper demonstrates how to apply computational fluid dynamics (CFD) simulation results to architectural design processes, and how the CFD-driven decisions impact ventilation and cooling: (1) background: A CFD helps predict the natural ventilation’s potential, the integration of CFD results into design decision-making has not been actively practiced; (2) methods: Pressure data on building facades were obtained from CFD simulations and mapped into the 3D modeling environment, which were then used to identify optimal positions of two openings of a zone. The effect of the selected opening positions was validated with building energy simulations; (3) results: The cross-comparison study of different window positions based on different geographical locations quantified the impact on natural ventilation effectiveness; and (4) conclusions: The optimized window position was shown to be effective, and some optimal solutions contradicted the typical cross-ventilation strategy.

The energy performance of a building is strongly influenced by its level of solar exposure, in turn affected by the climate, built context, and building morphological characteristics. Since these are typically fixed at the early- design phase, performance assessment methods based on solar considerations at the urban scale are essential to support early decision-making. As the adaptation of the well-developed building performance simulation methods to the urban scale lead to complexity issues, it is of interest to verify whether simpler metrics can act as performance indicators, as is often done at the building level with quantities such as form factor. This paper first presents a review of existing methods that aim at evaluating aspects such as passive heating and photovoltaic potential. We classify each evaluation metric based on its level of complexity, before choosing a representative subset to be further examined. The goal is to test whether simple metrics fulfill their role as performance indicators when applied to neighborhoods. To do so, the selected metrics, including geometrical parameters (e.g. compactness) and solar exposure levels (e.g. annual irradiation), are compared with simulation results (e.g. heating need), taken as reference values. This is done for two sets of designs: six distinct typologies and a large number of variants from a base case typology. Results, which show various levels of correlation between simple metrics and the reference values, highlight the limitations of using any one metric and indicate a need to revise the definition and evaluation metrics of various performance criteria for neighborhood designs.

ACL; International audience; Despite recent developments, neighborhood-scale performance assessment at the early-design phase is seldom carried out in practice, notably due to high computational complexity, time requirement, and perceived need for expert knowledge, ultimately limiting the integration of such a task into the design process. In this paper, we introduce a predictive modeling approach to rapidly obtain an estimate of the performance of early-design phase neighborhood projects, from simple geometry- and irradiation-based parameters. The performance criteria considered are the passive solar and daylight potential, respectively quantified by the energy need for space heating and cooling (given certain assumptions) and the spatial daylight autonomy at the ground-floor level. Two predictive models, or metamodels, are developed following distinct techniques: a multiple linear regression function and a Gaussian Processes regression model. These are developed from a reference dataset acquired through the parametric modeling and simulation of neighborhood design variants. When tested on designs provided by professionals, the metamodels with the highest accuracy within the compared types (MLR versus GPs) portray a prediction error below 10% in 87% (respectively 60%) of the cases for the passive solar (resp. daylight) potential. Results show this approach to be a promising alternative to running detailed simulations when comparing early-design variants. © 2017 Elsevier Ltd

The increasing popularity of parametric design tools goes hand in hand with the use of building performance simulation (BPS) tools from the early design phase. However, current methods require a significant computational time and a high number of parameters as input, as they are based on traditional BPS tools conceived for detailed building design phase. Their application to the urban scale is hence difficult. As an alternative to the existing approaches, we developed an interface to CitySim, a validated building simulation tool adapted to urban scale assessments, bundled as a plug-in for Grasshopper, a popular parametric design platform. On the one hand, CitySim allows faster simulations and requires fewer parameters than traditional BPS tools, as it is based on algorithms providing a good trade-off between the simulations requirements and their accuracy at the urban scale; on the other hand, Grasshopper allows the easy manipulation of building masses and energy simulation parameters through semi-automated parametric workflows. In this paper, we present a preliminary version of the developed plug-in and a typical design workflow for the simulation and visualization of building performance at the neighbourhood scale. We conclude by discussing its scalability to larger urban areas using 3D geodata as input and the coupling with Computational Fluid Dynamics (CFD) simulations and optimization algorithms.