A Multi-node Thermal Comfort Model Based On Chinese Thermo-biological Features

Human thermal comfort model was established on the base of physiology, bio heat transfer, environmental physiology and other studies. However, all the existing thermal comfort models were built based on the physiological and psychological data of western people. Through a comparison between model predictions and experimental studies on Chinese, it had been found the western based models were inadequate to predicate thermal sensation of Chinese people, some prediction errors were up to 1 scale. The prediction error indicated that a multi-node thermal comfort model based on Chinese physiological and psychological feature was in need. In this thesis, a Chinese physiological and psychological data based thermal comfort model was built and then validated through experimental studies on Chinese people. The established model was further used in air conditioning system design and development of air conditioning’s control strategy. Main contents and results of the study included:1) An individual thermoregulation model was established based on the Chinese thermophysical data. Chinese and westerners have significant differences in body size and composition, thus a standard Chinese model was firstly built based on the anthropometric and physiological studies, which was then individualized with four input parameters: height, weight, age and gender. Sensitivity analysis reveals that difference in height and weight can result in a difference of 1.2 °C in mean skin temperature, and difference in age can result in 0.5°C in mean skin temperature. Through experimental studies and data from publications, the thermal regulation model was validated. Compared with the standard Fiala model, modification of thermoregulation model with Chinese physiological characteristics can significant improve the prediction accuracy of skin temperature for Chinese. Maximum bias for the prediction of mean skin temperature decreased from 0.79 °C to 0.48 °C, and for local skin temperature changed from 2.11 °C to 1.46 °C. Further significant improvement was also found between the individualized and standard Chinese model. For the individualized model, mean bias of mean skin temperature between prediction and experiment is in the range of 0.20°C- 0.38°C, and most the standard deviation of local skin temperature prediction is less than 1°C.2) A thermal sensation model was established based on Chinese thermal psychological features. Based on the skin temperature calculated by the thermoregulation model, a local thermal sensation model was built. Local thermal sensation was influenced by local skin temperature and mean skin temperature, while in transient conditions local thermal sensation will also be affected by environmental changes. Thus a regression equation was developed, and a set of experimental data was used to determine the weighting coefficient of the above influences in the equation. Overall thermal sensation was then predicated by local thermal sensation according to the following principle: the more the local sensation deviate from neutral, the more important it in the predication of overall thermal sensation. Based on this principle, overall thermal sensation was calculated.3) Combining the thermoregulation and thermal sensation model, a Chinese thermal comfort model was established. Then we can predicate thermal sensation level input with environmental and personal parameters. Furthermore, considering residents from different climate zones will adapt to their surrounding nature and social environments, both physiologically and psychologically, an adaptable procedure was embedded in the Chinese model, which allows the user to adjust inputs based on subgroup difference in personal parameter and thermal preference. The Chinese thermal comfort model was then validated wildly by experiments on Chinese people. Through our own experiments results and data from publications, a database of thermal comfort study on Chinese was established. For uniform and stable environment, deviations between prediction and actual vote of thermal were within the range of ± 0.2 scale; in a dynamic environment, prediction of mean skin temperature were in the range of the standard deviation of the measured values, whereas most of the predicted overall thermal sensation deviation were mainly in the range of ±0.5 scale. Comparing with thermal sensation investigation in real buildings all across China, most of the correlation parameters between predicted and actual mean sensation vote were around 0.9.4) The Chinese thermal comfort model was applied in the design of indoor environment, like indoor air conditioning design parameters were then calculated based on the model. In summer cooling condition, for the seek of the same thermal comfort range, comparing with existing PMV model based standard, the design indoor air temperature based on the Chinese model can raise about 1-2°C. The Chinese thermal comfort model also could be used in the non-uniform indoor environment development, based on the prediction by the Chinese model, an optimized air supply parameters combination for a bedside air supply system was obtained. Furthermore, a thermal sensation based air conditioning system control strategy was developed using the Chinese thermal comfort model. Through questionnaire surveys and measurement of environmental parameters, the thermal sensation based control strategy was not just comfort enough and also had a better energy performance. The predicted thermal sensations were within the range of ± 0.5; and when comparing to traditional control strategy, for typical summer condition, energy saving rate was up to 14.8%.