Experimental and Numerical Insights into Indoor Contaminant Transport and Ventilation Performance: A Review

Indoor air quality depends on how airflow, buoyancy, and turbulence transport contaminants through occupied spaces. However, experimental diagnostics and computational fluid dynamics (CFD) are often applied in isolation. This review examines how the two can mutually constrain predictions of indoor contaminant transport and ventilation performance. A structured narrative review of Web of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed focused on studies published between 2020 and 2026, complemented by earlier benchmark studies. The evidence covers contaminant physics, flow and tracer-gas diagnostics, turbulence and multiphase modeling, ventilation strategies and metrics, and data-driven and digital-twin approaches. Probe intrusiveness and non-passive tracer behavior bias measured ventilation indices. Isotropic Reynolds-averaged Navier–Stokes (RANS) closures, such as standard k-ε, misrepresent buoyant plumes and overpredict near-wall particle deposition. The shear stress transport (SST) k-ω model and large eddy simulation (LES) improve fidelity at increased computational cost. Bulk air-change rates do not reflect breathing-zone exposure. Surrogate models and digital twins inherit the errors of insufficiently validated CFD training data. Key gaps include dynamic occupancy, unstandardized respiratory source terms, and inconsistent reporting of boundary conditions, grid verification, and measurement uncertainty. A hierarchical measurement–CFD–artificial intelligence (AI) validation framework is proposed to support the design of exposure-oriented ventilation, adaptive control, and energy-efficient operation.