Thermal Sensation Models Research Articles

A method of predicting the dynamic thermal sensation under varying outdoor heat stress conditions in summer

Heat stress events in urban areas are increasing as a result of global warming and urban heat islands. In response to heat stress, outdoor activators naturally often move themselves to a less hot place. An understanding of human physiological responses in dynamic outdoor thermal environments is desired. This study aims to reveal the dynamic physiological adjustment and thermal perception response characteristics under varying outdoor heat stress conditions. A robust model for predicting dynamic thermal sensation outdoors has been developed. Experiments involving heat stress changes in a hot summer were conducted with 25 subjects. Three categories of data were collected including meteorological data, physiological parameters, and thermal perception. The results showed that lower-arm skin temperature ( T lowerarm ) is more sensitive to changes in the outdoor thermal environment, and correlates closely with the thermal sensation vote ( TSV) . For a better practical application, based on the strong linear relationship between T lowerarm and T ty , the new dynamic outdoor thermal sensation model has been developed involving two parameters: T lowerarm and Δ T lowerarm /Δt (the change rate of T lowerarm ) . The validity of the model in transient outdoor conditions was verified. The algorithm can be integrated into a wearable armband to predict practical thermal sensation responses. This contribution will advance technologies based on the scientific findings to provide alert services to support human health and wellbeing, consequently increasing urban resilience and sustainability. • Experimental studies on heat stress were carried out under transient outdoor thermal environments. • The relationship between dynamic thermal sensation and physiological responses was revealed and quantified. • A dynamic outdoor thermal sensation model was developed with steady-state and dynamic terms. • The model can provide early overheating risk prediction during outdoor thermal exposure.

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation.

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

Thermal Perception and Physiological Responses under Different Protection States in Indoor Crowded Spaces during the COVID-19 Pandemic in Summer

Currently, people in crowded indoor spaces are required to wear a variety of personal protective equipment to curb the spread of COVID-19. This study aimed to investigate the effects of wearing four types of personal protective equipment (unprotected, wearing masks, wearing face shield and wearing medical protective clothing) on human thermal perception and physiological responses in indoor crowded spaces in summer. The experiment was conducted in a climate chamber designed to simulate the indoor crowded spaces. Environmental parameters of climate chamber (air temperature, relative humidity and wind speed), physiological parameters of subjects (wrist skin temperature and pulse rate), and subjective perceptions (thermal sensation and thermal comfort) were collected during the experiment. The experimental results showed that medical protective clothing has the most obvious blocking effect on heat exchange between human and environment. Thermal sensation in state 4 (wearing medical protective clothing) was significantly (p < 0.05) higher than that in other states. The study of physiological parameters showed that the wrist skin temperature and pulse rate under different protection states increased with the increase of room temperature. Through regression analysis, the thermal sensation estimation model of protective personnel in indoor crowded spaces based on wrist skin temperature and pulse rate was established. The adjusted R2 and RMSE of all models were above 82% and less than 1, indicating that the established thermal sensation model had a good prediction effect.

Prediction of the Impact of Air Speed Produced by a Mechanical Fan and Operative Temperature on the Thermal Sensation

Natural ventilation associated with a mechanical fan is a feasible strategy to enhance thermal acceptability in warm weather. The ASHRAE-55 provides the increase for operative temperature proportional to the increase in air speed while maintaining thermal comfort. Conversely, the range of informed values is limited and little guidance for mechanical fans is provided. This work explores the relationship between operative temperature and air speed produced by ceiling fans, and the effectiveness to deliver thermal comfort for a wider range of values. The research method comprises transient computer fluid dynamics simulations coupled with a thermal sensation model and is divided into two stages: a calibration exercise and a parametrical investigation. Three matrices are presented for a range of operative temperatures (21.0–36.0 °C) and air speeds (0–2.5 m/s) for: Dynamic Thermal Sensation (DTS) (a computer-based seven-point index), Predicted Percentage of Dissatisfied, and potential Cooling Effect. When compared to the Predicted Mean Vote, the DTS overestimates thermal comfort for temperatures under 28.0 °C with increased air speed and overestimates discomfort for temperatures above 31.0 °C, even with increased air speed. Agreement is found between both scales for 28.0–31.0 °C, defining a range for the effective use of ceiling fans to provide thermal comfort under warm weather conditions.

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach.

Machine learning models have been widely used for studying thermal sensations. However, the black-box properties of machine learning models lead to the lack of model transparency, and existing explanations for the thermal sensation models are generally flawed in terms of the perspectives of interpretable methods. In this study, we perform an interpretability analysis using the "SHapley Additive exPlanation" (SHAP) from game theory for thermal sensation machine learning models. The effects of different features on thermal sensations and typical decision routes in the models are investigated from both local and global perspectives, and the properties of correlation between features and thermal sensations and decision routes within machine learning models are summarized. The differences in the effects of features across samples reflect the effects of features on thermal sensations not only can be demonstrated by significant magnitudes but also by differentiation. The effects of features on thermal sensations often appear in the form of combinations of two to four features, which determine the final thermal sensation in most cases. Therefore, the neutral environment may actually be a dynamic high-dimensional space consisting of certain combinations of features in certain ranges with changing shapes.

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

To create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-the-art thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building’s heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.

Study on prediction model of thermal sensation under local draft in low pressure environment

Local draft experiments were conducted to establish the overall thermal sensation prediction model under low pressure environment. Head, chest, hand, and ankle were exposed to local airflow under three pressure environments: 1 atm, 0.87 atm, and 0.74 atm, respectively. The skin temperature was measured and the subjective questionnaires were collected. The results showed that in cooler environment, the mean skin temperature decreased with the decrease of pressure, and people maybe more sensitive. When the environmental pressure decreased, the overall thermal sensation decreased correspondingly. Compared with the normal pressure condition, as the pressure decreased, the weight of local thermal sensation on overall thermal sensation changed a little. The weight of chest decreased while the weight of hands increased. When exposed to local draft, the four body parts showed similar influence on the overall thermal sensation under lower pressure conditions. Based on influence of pressure, temperature, and local draft speed, the overall thermal sensation prediction model for each body part was established respectively. The results indicated that the predicted values were highly correlated with the measured values. The models could be used to predict thermal sensation of people who are exposed to local draft in low pressure environment.

Development of facial-skin temperature driven thermal comfort and sensation modeling for a futuristic application

The human body is governed by the physiological thermoregulation principle of balancing the heat flux between the body itself and the ambient thermal environment. A number of data-driven thermal comfort assessment approaches have recently been investigated based on the use of real-time sensing over the body, but a potential issue of intrusiveness in sensing locations has also been frequently reported. This research therefore explored the potential use of human facial skin temperature as primary physiological data to develop data-driven thermal comfort and sensation models that would be applied as a futuristic method without worrying about intrusive sensing location issues. This facial skin temperature-driven method has the potential to remove the issue of intrusive locations while providing a user-friendly approach by simply adopting a remote infrared sensor. This study adopted a series of environmental chamber tests with multiple participants recruited to consider the gender ratio in order to identify any significant differences that may be caused by the physical characteristics of human subjects.The average prediction accuracy for thermal comfort in the gradient boosting algorithm is 95.6% and 95.2% for thermal sensation in the data analysis of the first-round tests. Furthermore, data analysis revealed that the model prediction performance was around 80.4% in the validation experiments. Based on the test results, this study developed a facial-skin temperature-driven thermal comfort and sensation model, taking into account individual physiological characteristics, and also identified the most common facial skin areas that provide the best datasets for higher accuracy of comfort/sensation prediction.

Assessing The Thermal Comfort Conditions In Open Spaces: A Transversal Field Survey On The University Campus In India

Outdoor thermal comfort (OTC) promotes the usage frequency of public places, recreational activities, and people's wellbeing. Despite the increased interest in OTC research in the past decade, less attention has been paid to OTC research in cold weather, especially in arid regions. The present study investigates the OTC conditions in open spaces at the campus area in the arid region. The study was conducted by using subjective surveys(questionnaire) and onsite monitoring (microclimate parameters). The study was conducted at the Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana-India campus during the cold season of 2019. The timings of surveys were between 9:00 and 17:00 hours. The authors processed the 185 valid questionnaire responses of the respondents to analyze OTC conditions. Only 8.6% of the respondents marked their perceived sensation "Neutral." Regression analysis was applied between respondents' thermal sensations and microclimate parameters to develop the empirical thermal sensation model. The air temperature was the most dominant parameter affecting the sensations of the respondents. The empirical model indicated that by increasing air temperature, relative humidity, and solar radiation, the thermal sensations also increased while wind speed had an opposite effect. Physiological equivalent temperature (PET) was applied for assessing the OTC conditions; the neutral PET range was found to be 18.42-25.37°C with a neutral temperature of 21.89°C. The preferred temperature was 21.99 °C by applying Probit analysis. The study's findings could provide valuable information in designing and planning outdoor spaces for educational institutions in India's arid regions

Exercise Thermal Sensation: Physiological Response to Dynamic-Static Steps at Moderate Exercise.

The study of exercise thermal sensation is more difficult than that of static thermal sensation in the human body. This work’s main purpose was to examine specific changes in human physiological parameters and subjective perceptions during the exercise process, especially around dynamic–static steps, and to assess exercise thermal sensation. Experiments were conducted in a climate chamber. A total of 16 subjects participated in two activities of different intensities on a treadmill, namely at 4.5 km/h and 6 km/h. The experimental procedure was set to static–dynamic–static. Skin temperature (Tsk), oral temperature (Tor), heart rate (HR), heart rate variability (HRV) parameters, and electrodermal activity (EDA) were measured at fixed time points, and thermal sensation values, thermal comfort values, and sweat feeling index were collected. The results showed complex changes in physiological indicators around the dynamic–static steps. Some important physio-logical indicators can be used as valid parameters for exercise thermal sensation models, such as Tsk, Tor, and EDA. This study highlighted that prediction models using average change and rate of change of measurements were better than using the original measurements. Our findings suggest that the exercise thermal sensation prediction models should be constructed according to the dynamic–static state and that psychological factors cannot be ignored.

Evaluation of thermal sensation models for predicting thermal comfort in dynamic outdoor and indoor environments

Thermal sensation models are commonly used to assess thermal perception in various indoor environments. Our previous work developed a new model to predict thermal sensation in cars that uses gradual change in thermal load on the face, sudden change in solar radiation on the face, mean skin temperature and outdoor air temperature as predictors. The present investigation selected 11 outdoor scenarios and 20 indoor scenarios from the literature to further verify the accuracy of the thermal sensation model. Four other thermal models, the predicted mean vote (PMV) model, the dynamic thermal sensation (DTS) model, a model from the University of California, Berkeley (UCB), and a transient outdoor thermal comfort model (Lai's) were compared with the new model for the 32 scenarios. The results confirmed the validity of the new model in an outdoor environment with sudden change in solar radiation. The new model was able to predict the trend of thermal changes, but the accuracy was not as good as that of the PMV model in an environment with indoor temperature gradient/sudden changes.

A Random Forest Classification Algorithm Based Personal Thermal Sensation Model for Personalized Conditioning System in Office Buildings

Abstract The personal thermal sensation model is used as the main component for personalized conditioning system, which is an effective method to fulfill thermal comfort requirements of the occupants, considering the energy consumption. The Random Forest classification algorithm based thermal sensation model is developed in this study, which combines indoor air quality parameters, personal information, physiological factors and occupancy preferences on selection of 7-level of sensation: cold, cool, slightly cool, neutral, slightly warm, warm and hot. Our model shows better functionality, as well as performance and factor selection. As a result, our method has achieved 70.2% accuracy, comparing with the 57.4% accuracy of support vector machine, and 67.7% accuracy of neutral network in an ASHRAE RP-884 database. Therefore, our newly developed model can be used in personalized thermal adjustment systems with intelligent control functions.

Proposal of Relative Thermal Sensation: Another Dimension of Thermal Comfort and Its Investigation

Real-time thermal comfort evaluation is not only essential in constructing the control module of Heating, Ventilation and Air Conditioning (HVAC) systems in residential buildings but also rather critical in energy conservation. In the transient thermal environment, current thermal comfort is not stable and varies from time to time. Therefore, if we only evaluate the current thermal sensation that will cause prediction error. Existing thermal comfort models mainly focus on the evaluation of current thermal comfort. However, research on the evaluation of thermal sensation variation trend is vacant. Furthermore, since individual differences play an important role in thermal comfort evaluation, physiological indices should be considered. To solve this problem, in this paper, the authors exclusively propose the concept of relative thermal sensation which accounts for the thermal sensation variation trend and give its real-time evaluation method by analysis of skin/clothes temperatures of ten local body segments using machine learning algorithms. By incorporating the relative thermal sensation model with an ordinary thermal comfort model, a novel complex thermal comfort model is derived, which has the ability to predict the current thermal comfort and the thermal sensation variation trend simultaneously and provides an early warning mechanism for thermal discomfort.

Thermal sensation under high-intensive exercise in naturally ventilated gymnasiums in hot-humid areas of China: Taking basketball players for example

As a public building for exercises and entertainments, gymnasiums play an important role in people's daily life. In regions with hot-humid climate, thermal sensation in gymnasiums is directly related to the human health as well as energy consumption. However, little can be found in the systematic evaluation system reported for thermal sensation of athletes in gymnasium in hot-humid climate regions; therefore, there is a need to develop a thermal sensation model for evaluating thermal sensation of athletes partaking sports in these environments. In this study, a field survey on thermal sensation of players in a basketball game was carried out in a naturally ventilated gymnasium in Guangzhou, China. The results showed that the human physiology and thermal sensation of subjects were highly different during active sport state. Moreover, the thermal sensation vote was increased with a rise in the air temperature, the mean radiant temperature and humidity ratio, while thermal sensation vote was reduced with an increase in the air velocity. The metabolic rate, systolic blood pressure and skin temperature were shown to have a positive correlation with the thermal sensation vote. According to the analysis, a predictive thermal sensation model, which has been developed by this study to estimate the thermal sensation under high-intensive sport activities in gymnasiums in regions with a high temperature and high humidity, was evaluated and validated.

Thermal sensation model for driver in a passenger car with changing solar radiation

Thermal sensation in cars is different from that in buildings. Transient, asymmetric solar radiation and transient, non-uniform air temperature are the main causes of the difference. This investigation conducted human subject tests with 24 subjects, 62 trials under three outdoor driving conditions. These three driving conditions refers to 1) highly transient environments during the cool-down phases in summer, 2) highly transient environments during the warm-up in winter, and 3) sudden changes in solar radiation in shoulder season. Then the data were used to evaluate the performance of four thermal sensation models: the predicted mean vote model, the dynamic thermal sensation model, a model from the University of California, Berkeley, and a transient outdoor thermal sensation model. The results of the evaluation indicated that none of the models could accurately predict thermal sensation in a car. The sudden change in solar radiation experienced by the driver was identified as an important factor in this discrepancy. Therefore, this study proposed a new thermal sensation model that incorporates the change in the driver's thermal load caused by a sudden change in solar radiation as a predictor. This investigation verified the validity of the new model in a transient and non-uniform vehicular thermal environment.

The SPA-TOPSIS-Based Evaluating Approach on Thermal Sensation Model at Different Vane Angles in UFAD–DV System

Under floor air distribution system and displacement ventilation (UFAD–DV) system is used to provide comfortable indoor thermal environment in summer, which largely satisfies overall and local thermal sensation. In UFAD–DV system, the selection of supply vane angle is particularly important to achieve the best thermal comfort. This paper studied thermal sensation of a manikin in sedentary state through the AIRPAK simulations. Firstly, in order to analyze the influence of the local thermal sensation on overall thermal sensation (OTS), the manikin model consisting of five local body parts was constructed. Then, the set pair analysis technique for order preferences by similarity to an ideal solution (SPA-TOPSIS) method was proposed to analyze the overall thermal sensation model and the combination model of overall and local thermal sensation at different supply vane angles. Finally, according to the SPA-TOPSIS analysis, the combination model of overall thermal sensation and local thermal sensation was determined and the best supply vane angle was 10° downward. The PMV and PPD values of each part of the body and overall body were 0.1932, 0.3413, 0.2287, 0.0503, − 0.0992, 0.173 and 5.9721, 7.5005, 6.377, 5.2632, 5.2579, 6.79, respectively. The weight coefficient method was used for the validation of SPA-TOPSIS-based evaluating results with p < 0.05. In addition, the thermal sensation of foot has important impact on thermal sensation, and the ideal integrated thermal sensation of five local body parts and overall body was achieved. The proposed SPA-TOPSIS-based evaluation approach can determine the best supply vane angle, which optimizes the OTS on the premise of satisfying the local thermal sensation.

Effect of long-term thermal history on physiological acclimatization and prediction of thermal sensation in typical winter conditions

Thermal history plays an important role in thermal comfort of occupants. In this study, climate chamber experiments were conducted in winter using two groups of total 32 subjects who have different indoor thermal histories. The cold-adapted (CA) group consisted of subjects who long lived in naturally ventilated buildings (approximately 11.8 ± 3.4 °C in winter) in the hot summer and cold winter climate zone of China. The warm-adapted (WA) group consisted of subjects who long lived in heated space in winter. Two kinds of thermal conditions were investigated: the temperature dropping (24-16 °C) and constant severe cold (12 °C) conditions. Results show the thermal sensation vote and heart rate of the CA group were significantly different from that of the WA group in a short period when temperature just dropping to 16 °C and in most time of severe cold condition of 12 °C. However, there were no difference in skin temperatures and blood pressure between the CA and WA groups during the whole experiment period in both conditions. Thus, a cold-adapted basal heart rate was proposed to calculate metabolic rate for the CA group. Results show the accuracy of predicted thermal sensation model could be improved while considering the factor of cold acclimatization.

Development of the Predicted Thermal Sensation (PTS) model using the ASHRAE Global Thermal Comfort Database

In this study, existing thermal comfort models were reconsidered and analysed. A new predicted thermal sensation (PTS) model was developed, in which Gagge's two-node model was used for the calculation of thermal regulation. This model was established using data from the ASHRAE Global Thermal Comfort Database and the index of standard effective temperature (SET). This model could not only predict human thermal sensations in various environmental conditions, but also reflect the discrepancies in thermal adaptation in different taxonomies. PTS models were developed for the classification of different climatic zones in China. In addition, worldwide PTS models were proposed for the typical climate types. Some of the data from field studies were introduced to validate these models. The rationality and accuracy were demonstrated, indicating that the PTS model was a practical, flexible, and effective choice for guiding building construction, as well as for evaluating an actual thermal environment. Moreover, further directions for the model's future development were indicated.

Study on Outdoor Thermal Comfort in the Transitional Season of Hefei

In order to study the outdoor thermal comfort during the transition season in Hefei, a university in Hefei adopted a combination of field environmental measurements and questionnaires to study the changes in thermal sensation and thermal comfort of outdoor people before and after the transition season. The rankings of the effects of temperature, wind speed, humidity, and solar radiation on human thermal comfort were obtained through surveys, and the proportion of each parameter’s influence on human thermal comfort was analyzed. The relationship between thermal sensation and thermal comfort was analyzed, and the application was established through regression analysis Prediction model of thermal sensation in autumn and winter outdoor environment in Hefei area.

DISCUSSION ON A MODEL OF TWO TURNING POINTS AS EXPRESSION OF INDIVIDUAL VARIATION

Many human-environmental models were proposed by researchers of environmental psychology and surrounding fields, in order to deepen the understanding. Proposal of those models often defines and differentiate similar conditions and/or expand the usage of the former proposed model. For example, Kuno et al proposed two-dimensional thermal sensation model that introduced surrounding environmental index axis and thermal physiological index model, which differentiated “hot” to “warm” and “comfort” to “pleasant”. Likely, model of two turning points introduced by Hirate et al differentiated the type of decision, namely one turning point and two turning points. One turning point is where decision only changes once, like good / bad. Two turning points was where decision changes twice, like bad / good / bad, which indicates the adequate range. In addition, this model was developed from model proposed by Thustone, which adapted the model to experiments done by pairwise comparison method. Hirate et al not only introduced the model with two turning points as an expansion of the model, but also developed the model to explaining the experimental results of semantic differential method.