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Open Access Publications from the University of California

Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning


Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. However, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces. First, the coupled CFD model is developed to study wind-driven cross ventilation in high-density cities. A 3D isothermal CFD simulation is solved using the RNG k-ε turbulent model. The simulation results are compared with wind tunnel experiment data from literature. Second, six key design variables are used to generate 3840 parametric design variations for natural ventilation assessment. Third, a novel integrated index CIOIv (coupled indoor and outdoor interaction) is proposed to evaluate the wind speed ratio between the indoor area and outdoor reference area. For demonstration, CIOIv,F1 is used to represent CIOIv on the ground level. Data-driven CIOIv,F1 models are developed to predict indoor building ventilation potential for quick early design support. Compared with multivariate linear regression model, the Gradient Boosting non-linear model displays much higher prediction accuracy (mean absolute percentage error = 0.16 with R2 = 0.8). In early design stage, designers and engineers can skip the computational expensive CFD simulations and use this data-drive model to quickly check the building natural ventilation potentials of different design options in an urban environment.

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