Correlation of experimental data and simulation models in adaptive thermal comfort analysis for university classrooms
Year: 2025
Type: article
Abstract: HVAC vent configuration is a critical element of building design in shaping indoor air distribution and thermal comfort. Since thermal comfort is subjective and varies by region, this study aims to develop an adaptive thermal comfort model using SolidWorks CFD for an air-conditioned classroom at Swinburne University of Technology, Sarawak, Malaysia. Previous studies often relied on the PMV model and steady-state assumptions, which are less accurate in tropical climates and fail to account for transient temperature changes. Therefore, experimental measurements were used to define boundary conditions and validate simulation results to optimize the existing model. Results emphasize the importance of optimizing supply air temperature boundary conditions to account for transient effects. This study also incorporates Thermal Sensation Vote (TSV) data to provide region-specific insights and uses experimental data to determine suitable boundary conditions. The optimized model lowered temperature prediction errors from 16% to 6% in unoccupied conditions and from 8% to 4% in occupied conditions during transient analysis, while satisfying ASHRAE Standard 55 air velocity requirements. The PMV-TSV discrepancy of -0.62 underscores the need to calibrate PMV predictions for more accurate thermal comfort perceptions in Malaysia’s tropical climate. Improved vent configurations reduced airflow short-circuiting and decreased temperature variations from 23.9–25.0°C to 23.9–24.1°C, resulting in a more balanced indoor environment.
Source: Energy and Buildings
Authors: Jason Zhi Cheng Ting, Charlie Chin Voon Sia, Elaine Yee Lee Yeu, Ping Ping Chung, Saravana Kannan Thangavelu, Annie Anak Joseph
Institution: Universiti Malaysia Sarawak
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,260
OpenAlex ID: W4414946407