A Superposition-Based Method for Rapid Estimation of Indoor Airflow Fields Under Occupant Movement

Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada

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Abstract

Indoor air quality is strongly affected by the behavior of airborne pollutants such as pathogens dispersed in circulating air. The dispersion of these particles depends heavily on the airflow field and the vortices generated within an enclosed space. While predictable factors—such as ventilation configuration and furniture layout—can be investigated through one-time numerical simulations, unpredictable factors, particularly occupant movement, remain a major challenge. Previous studies have attempted to simulate airflow under specific movement scenarios; however, their results are case-specific, lack generalizability, and require extensive computational resources. To address this limitation, the present study introduces a novel method for rapidly estimating airflow fields influenced by occupant movement. The approach is based on a dataset generated from computational fluid dynamics (CFD) simulations of a moving person at different speeds. These simulations capture the vortical structures created around the body, represented by distributions of velocity vectors at computational nodes. When occupant movement occurs in a ventilated domain, the stored velocity vector distributions can be superimposed onto the baseline airflow field of the empty room. By combining these two components—the airflow induced by the ventilation system and the flow generated by occupant motion— the method reconstructs the overall velocity field around a moving person in a ventilated environment. This superposition technique substantially reduces computational cost by avoiding the need for repeated CFD simulations for every new movement scenario. At the same time, it provides timely and reliable predictions of airflow dynamics under occupant-induced disturbances. The proposed method therefore offers a practical and efficient tool for assessing pollutant transport and infection risk in real indoor environments where human activity introduces inherently uncertain airflow conditions.