Driven by increasing demands for vehicle thermal comfort, intelligent HVAC and localized thermal management systems are widely adopted in modern vehicles. Their design relies on thermal comfort models that balance predictive accuracy and practical deployability. This study systematically evaluates 13 widely used vehicle thermal comfort models using a unified framework covering four criteria: theoretical completeness, predictive accuracy, generalizability, and measurement feasibility. Experimental validation was conducted under four representative scenarios, including automatic HVAC operation in winter, HVAC combined with seat heating in winter, automatic HVAC cooling in summer, and HVAC with localized solar exposure in summer. The results show that only four models account for transient dynamics, and most neglect local thermal sensation, limiting their applicability in non-uniform cabins. Models incorporating skin temperature outperform environment-only approaches, achieving prediction errors below 1 RMSE across multiple scenarios. The best-performing hybrid model achieved an RMSE of 0.34 under localized solar exposure, whereas environment-only models often exceeded an RMSE of 1. Hybrid models provide the most balanced trade-off between accuracy, generalizability, and feasibility. These findings highlight the need for future vehicle thermal comfort models that integrate dynamic physiological responses while remaining practical for real-time applications.

During the COVID-19 pandemic, hospital wards served as crucial medical infrastructure for patient treatment. The existing ventilation systems in hospital wards are often criticized for their high energy consumption and poor pollutant removal. This study introduces an improved active chilled beam (ACB) system for wards. This system integrates an ionization module with the ACB, utilizing the ACB’s supply air jet and exhaust air convergence to create a directed airflow within the ward, effectively purifying aerosols. First, a validated CFD model is employed to simulate airflow generated by the ACB and the movement of aerosols exhaled by patients. Particle image velocimetry (PIV) is then used to visualize the indoor directed airflow created by the ACB. The targeted aerosol removal performance of the ACB system is quantitatively described using the TARGETING index. To address the cumbersome calculation of the TARGETING index, a qualitative rapid identification method for targeted ventilation is proposed. Finally, the purification effect of the ACB system in multipatient wards was optimized through theoretical analysis. Compared with existing systems, the improved ACB system enhances the targeted aerosol removal performance by 41.9%–62.5%, and achieves energy savings of 33.3%. In multipatient wards, the ACB system can reduce the cross-infection risk by 80.5%–91.0%.