An Empirical Investigation Of Airflow Characteristics And Thermal Comfort Using Computational Fluid Dynamics

Abstract

Thermal comfort and indoor air quality remain critical determinants of occupant wellbeing, productivity, and energy 
consumption in built environments, motivating a growing body of empirical work that couples experimental 
measurement with Computational Fluid Dynamics (CFD) simulation. This study presents an empirical investigation 
of airflow characteristics and thermal comfort within a mechanically ventilated indoor space, using a validated CFD 
model built on the Reynolds-Averaged Navier-Stokes (RANS) k-epsilon turbulence closure. Air velocity, temperature 
distribution, turbulence kinetic energy, and Predicted Mean Vote (PMV) indices were simulated across a range of air 
change rates (ACH 4 to 12) and inlet velocities (2 to 4 m/s), and the simulated results were validated against 
experimental measurements collected using hot-wire anemometry and calibrated thermocouples [1]. Five categories 
of tabulated data and five supporting figures are analyzed to characterize the relationship between ventilation 
parameters and occupant comfort. Results indicate that increasing ACH from 4 to 10 shifts the PMV index from a 
warm discomfort zone (PMV = 1.35) into the ASHRAE-recommended comfort band (-0.5 to +0.5), while excessive 
ACH beyond 10 introduces localized draft discomfort due to elevated air velocity near occupant zones. The CFD 
model demonstrated strong agreement with experimental data, with a mean absolute deviation of 0.31°C and 
correlation coefficient of 0.97. These findings establish a quantitative, data-driven relationship between ventilation 
design parameters and thermal comfort outcomes, offering practical guidance for HVAC system optimization. The 
study concludes that CFD-based empirical validation provides a reliable and cost-effective pathway for optimizing 
indoor airflow design without extensive physical prototyping [2]