Airflow modelling for building design: A designers' review

Ran Zhang, Xiaodong Xu, Ke Liu, Lingyu Kong, Wei Wang and Thomas Wortmann

Renewable and Sustainable Energy Reviews, 2024, vol. 197, issue C

Abstract: Sustainable design is no longer an emerging debate. Data-driven urban and architectural design faces several potential threats related to airflow modelling, including extreme wind environments, pollutant emissions, and the potential for new wind energy in design. Designers require advanced design tools to address these airflow modelling challenges. Still, it is difficult for designers to implement and utilise airflow models fully, and not all data-driven approaches are accessible to designers in traditional design workflow systems. This review systematically analyses the frontiers of airflow modelling in urban and architectural building design from the designer's perspective. It briefly summarises the advantages and disadvantages of airflow modelling based on the parametric design's white-box, black-box, and grey-box approaches. This review explores the challenges of wind environments and their analytical responses by screening design research articles at three scales. It summarises the eight main existing research directions. The article discusses the findings of designers at each scale, revealing significant areas of interest and continuing focus, such as the reduction of pollutant emissions and the interaction of new wind energy generation in design. This study concludes with three responses to possible challenges: improved resolution for design research loop, unification of information entropy in design and research, and airflow modelling tool-accessibility in the overall design ethos and education mindset; and provides illustrative vignettes of possible demos for the future development of airflow models' accessibility; and offers a vision of how designers better produce data-accessible design logic and fulfil their role in future sustainable design.

Keywords: Building design; Airflow modelling; Design research; Computational fluid dynamics; Renewable energy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114380

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