Preliminary design geometry for predicting natural ventilation performance in row houses

Khotijah Lahji; Agus Budi Purnomo; Inavonna

doi:10.30822/arteks.v11i3.5726

Authors

Khotijah Lahji Trisakti University
Agus Budi Purnomo Trisakti University
Inavonna Trisakti University

DOI:

https://doi.org/10.30822/arteks.v11i3.5726

Keywords:

Computational Fluid Dynamics (CFD), Initial design geometry, Natural ventilation, Predictive model linear regression, Row houses

Abstract

The Computational Fluid Dynamics (CFD) prediction tool has demonstrated a high degree of accuracy in modeling airflow velocity; however, its application requires comprehensive design data, making it more appropriate for use during advanced stages of the architectural design process. At the conceptual design stage, tools capable of predicting airflow velocity remain relatively limited. Therefore, this study aims to develop a geometry-based prediction tool that is simple, accessible, rapid, practical, and sufficiently accurate for early-stage architectural design. A quantitative research approach was employed, beginning with CFD simulations based on geometric or independent-variable indicators across 212 cases of two-story row house designs to obtain accurate airflow-velocity values as the dependent variable. The processed initial-design geometry data and CFD simulation results were subsequently analyzed using linear regression through the enter and stepwise methods in SPSS. The initial prediction equation was selected based on the model with the highest R² value and the smallest number of determining variables. The findings yielded an R² value of 0.738, determined by four geometric or independent variables: (1) building width (-), (2) number of doors on the second floor, (3) width of the window inlet on the front facade of the second floor, and (4) area of the vertical stair void. This prediction tool is effective for application during the conceptual design stage. A further model produced an R² value of 0.867 based on 16 geometric or independent variables; although this model provides a higher level of predictive accuracy, its greater complexity makes it more suitable for application during the advanced design stage. This study addresses the existing gap in early-stage prediction tools by introducing a novel building-geometry-based predictive approach in the form of a linear equation that is practical, rapid, straightforward, and sufficiently accurate for supporting natural ventilation assessment during the early design stage without compromising architectural design creativity.

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