A Fourier neural operator-based method for rapid prediction of 3D indoor airflow dynamics

Indoor airflow distribution significantly influences temperature regulation, humidity control, and pollutant dispersion, directly impacting thermal comfort and occupant health. Accurate and efficient prediction of airflow fields is essential for optimizing ventilation systems and enabling real-time control. However, existing computational approaches for dynamic ventilation are computationally intensive and have limited generalization capabilities. This study leverages the Fourier neural operator (FNO), a method rooted in operator learning and Fourier transform principles, to develop a three-dimensional (3D) airflow simulation model capable of predicting velocity and its components. The model was trained using 200 s of sinusoidal ventilation data (amplitude: 0.4) and evaluated under diverse air supply patterns, including sinusoidal (amplitude: 0.8), intermittent, and stepwise periodic ventilation. Additionally, the model’s performance was assessed with low-resolution training data and further tested for recursive prediction accuracy. Results reveal that the FNO method achieves high accuracy, with a mean square error of 9.906 × 10^–5 for sinusoidal amplitude 0.8 and 4.004 × 10^–5 over 400 time steps for sinusoidal, intermittent, and stepwise conditions. Further evaluations, including tests on low-resolution training data and recursive prediction, were conducted to examine the model’s resolution invariance and assess its performance in iterative forecasting. These findings demonstrate the FNO method’s potential for robust, efficient prediction of 3D unsteady airflow fields, providing a pathway for real-time ventilation system optimization.