Multi-indicator Optimization of Kitchen Comfort Based on the C-index

Kitchens, as typical indoor environments characterized by intense thermal disturbances and complex ventilation conditions, face challenges in optimizing thermal comfort due to the need to account for multiple indicators and the risk of subjective or inconsistent weighting. This paper proposes a dynamic evaluation method based on Data Envelopment Analysis (DEA), which integrates four key indicators: W-PMV, ADPI, air age, and Ventilation Effectiveness, to establish a C-index for quantifying kitchen comfort.  The model is validated using CFD simulations and experimental measurements, and the thermal environment is systematically evaluated for airflow angles from ۳۵° to ۷۵°. The results show that the ۴۵° airflow angle achieves the highest score in summer (C=۰.۹۹) and remains effective in winter (C=۰.۸۶), suggesting good year-round applicability. Furthermore, a fan system is introduced based on the ۴۵° setup in summer, and a comparison of different installation methods reveals that corner installation improves the ADPI by ۱۶.۷% and reduces air age by ۰.۴۱ seconds, effectively mitigating localized ventilation dead zones. The study indicates that the DEA method can adaptively weight multiple indicators, and the proposed evaluation system provides data-driven, dynamic decision support for optimizing kitchen thermal environments.

کلیدواژه ها:

Data envelopment analysis (DEA) ، Multi-objective optimization ، Air conditioning-fan collaborative optimization ، Kitchen ventilation system ، Airflow organization