Evaluation of Four Models for Predicting Thermal Sensation in Chinese Residential Kitchen

Xiaojie Zhou,Sumei Liu,Qingyan Chen,Ke Qing,Weizhen Zhang,Jiankai Dong,Jian Li,Xuan Liu,Dayi Lai,Xiaorui Lin,S.I Tanabe,L Mazzarela,C Inard,G Cao,J Kurnitski,I Nastase,H Zhang,P Wargocki,M.C Gameiro Da Silva

doi:10.1051/e3sconf/201911102004

Abstract

Thermal environment in residential kitchen in China is transient and non-uniform and with strong radiation asymmetry from gas stove. Due to the complexity of kitchen thermal environment, it is not sure if previous thermal comfort models can accurately predict the thermal comfort in residential kitchens. In order to evaluate if existing thermal comfort models can be applied for Chinese kitchens, this investigation conducted human subject tests for 20 cooks when preparing dishes in a kitchen. The study measured skin temperatures of the cooks and environmental parameters and used questionnaires to obtain their thermal sensation votes at the same time. The actual thermal sensation votes were compared with the predicted ones by four thermal comfort models: predicted mean vote (PMV) model, dynamic thermal sensation (DTS) model, the University of California at Berkeley (UCB) model, and the transient outdoor thermal comfort model from Lai et al. The results showed that all the models could predict the trend of the thermal sensations but with errors. The PMV model overpredicted the thermal sensations. The UCB and Lai’s models showed a slower change in thermal sensation votes (TSV) after turning on the stove. The DTS model was more accurate than the others in predicting the mean thermal sensation, but with a large variation in predicting individual thermal sensation votes. A better thermal comfort model should be developed for Chinese residential kitchens.

Highlights

Cooking is a very important daily activity for a family
These three models are (1) the dynamic thermal sensation model (DTS) from Fiala [9], which was based on regression analysis of thermal sensation votes from experiment in a climate chamber and human physiological responses calculated from a multi-segment human heat transfer model; (2) the University of California at Berkeley (UCB) model [10], which was based on large-scale
The results showed that 60.8%, 71.1%, 78.7%, and 83.9% of the predicted votes by predicted mean vote (PMV), UCB, Lai’s and DTS models, respectively, had a difference of less than one unit from the actual thermal sensation votes (TSV)

Summary

Introduction

Cooking is a very important daily activity for a family. The average time for a family member spent in kitchen in China is about 3.6 hours per day [1, 2]. The use of high power gas stove in Chinese residential kitchen generates a lot of heat, which would deteriorate the thermal environment [3]. The most frequently used thermal comfort model for kitchen is the predicted mean vote (PMV) index [5, 6] that could further calculate predicted percentage dissatisfied (PPD). Since the thermal environment in a residential kitchen in China is transient and non-uniform due to strong radiation asymmetry from gas stove, PMV may not accurately evaluate the thermal comfort level. To evaluate if the above four models (PMV, DTS, UCB, and Lai’s) could be used to predict thermal comfort in Chinese residential kitchens, this study collected data from 20 cooks through human subject tests and compared the predicted thermal comfort level with the actual thermal sensation votes

Methods

Results

Discussion

Conclusion