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Evaluation and improvement of the thermoregulatory system for the two-node bioheat model
Abstract
The two-node bioheat model is widely used in thermal comfort standards and design tools. In recent years, there have been many new experimental studies and thermoregulatory models developed under stressful heat or cold conditions, but those have not been tested under the two-node model structure. Furthermore, limited validation studies of the two-node model revealed significant discrepancies in the prediction of skin temperature. This study collects relevant thermoregulatory models (six for sweating, three for skin blood flow and shivering, and four for sweat evaporation efficiency) and devises a methodology to compare the accuracy of various model combinations against experimental data. An improved model is developed and validated under heat and cold exposure conditions. The RMSE method is used to compare the accuracy of various model combinations and to optimize the proposed thermoregulatory model constants. The results reveal that only several model combinations can be considered as accurate for the core and skin temperature predictions, amongst which are the proposed models at the first rank.
... After selecting the appropriate thermal comfort and bioheat models, the next step is to conduct steady-state simulations under typical indoor conditions and then process and analyze the simulation results to establish the potential links between thermal comfort levels and physiological variables. In this paper, the new MPMV comfort index of [13] and the two-node bioheat model of [60] are selected to perform the analysis. In the following, further details have been added to the published work of MPMV [13] to account for non-zero mechanical work and heat removal from or addition to the body core section (readers are requested to consult the original publication for other details). ...
... In the following, further details have been added to the published work of MPMV [13] to account for non-zero mechanical work and heat removal from or addition to the body core section (readers are requested to consult the original publication for other details). Furthermore, some improvements have been introduced to the thermoregulation system of the two-node bioheat model of [60]. ...
... However, solving this equation will require additional thermoregulatory equations for the skin blood flow, NST rate, and regulatory evaporative sweating rate. To this end, the simplified two-node bioheat model of Ji et al. [60] is adopted, but with some modifications to increase its prediction accuracy. The following proposed modifications are introduced: ...
Current thermal comfort constructs are perceptional, and their relationships to the physiological responses are not fully understood. The latter are very important, not only to indicate personal comfort perception but also to protect personal health against cold and heat stresses. This paper combined a two-node bioheat model with the metabolic-based predicted mean vote index (MPMV) to uncover the relationships between the physiological responses and perceptional thermal sensation at steady-state conditions. The MPMV index accounts for sweating and non-shivering thermogenesis (NST) at the neutral comfort state and handles both core and skin cooling cases. Simulations were conducted for adults in typical indoor environment conditions. The physiological responses investigated included the body, core and mean skin temperatures, skin evaporative heat flux, shivering metabolic rate, and skin blood flow. The study revealed that only the mean skin temperature and skin blood flow can discriminate between comfort perception levels and can therefore be used alone or combined with other variables as alternative physiological indicators. The comfort range (MPMV within ±1) in terms of the mean skin temperature is maintained by regulatory sweating and/or NST and gets wider with increasing activity levels. The study also offered important insights for practical applications and future research.
... It can be applied to dynamic conditions and it can provide the thermal quantities of the human body, for example, the skin temperature and wittedness (Zhang and Lin, 2020). SET has been widely used for the evaluation of thermal comfort of indoor space and development based on the two-node model of SET has been performed to extend its usage under more scenarios (Ji et al., 2021;Nazarian et al., 2017;Zhang and Lin, 2020). Some recent studies have adopted SET for the assessment of building overheating (Ji et al., 2021;Laouadi et al., 2020b) to provide a more detailed description of the status of the human body under hot or warm conditions. ...
... SET has been widely used for the evaluation of thermal comfort of indoor space and development based on the two-node model of SET has been performed to extend its usage under more scenarios (Ji et al., 2021;Nazarian et al., 2017;Zhang and Lin, 2020). Some recent studies have adopted SET for the assessment of building overheating (Ji et al., 2021;Laouadi et al., 2020b) to provide a more detailed description of the status of the human body under hot or warm conditions. ...
... Therefore, SET cannot be calculated by a simple equation, but an iterative procedure is needed. Although there are already new studies proposing new versions of the SET (Ji et al., 2021), the original SET calculation program described in ASHRAE 55(ASHRAE-55, 2017) has been used in this study. SET considered not only the environmental parameters, including the air temperature, relative humidity, airflow velocity, radiant temperature, but also the characteristics of the individual occupants, which includes the metabolic rate and clothing insulation. ...
Due to urbanization and global warming, extreme heat events, e.g., heat waves, in the urban area tend to occur much more intensively and frequently, imposing a great threat to the health and safety of urban dwellers. The warming temperature also leads to a deteriorating of the indoor thermal conditions. Studies on indoor overheating should be conducted to figure out the interactions between the outdoor environment and indoor conditions and estimate possible approaches to
optimize the design and operation of the building system adapting to the changing climate. Considering the building and construction assets are typically designed for a long period over several decades or even centuries. The resiliency of the buildings to extreme conditions should be reconsidered under the future climate conditions, therefore the future weather inputs should be critical for the building overheating study, and any possible mitigation intervenes should be reevaluated under different future scenarios to evaluate the robustness of the decision and detect the potential risks. The study includes a procedure to select buildings from Montreal city for the field monitoring study, followed by the outcome of building information surveys and site visits. After an overall evaluation of these buildings, 6 school buildings, 6 hospital buildings and 3 residential social housings have been selected for further studies. The measured data are used for the overheating assessment of these buildings and the calibration of these real building models. The measured data exhibited strong evidence of overheating in existing building stocks in Montreal,
showing the necessity for further investigation to mitigate the overheating. To consider the spatial impact of urban climate, the study developed a high-resolution regional climate model for the Montreal and Ottawa region and elaborated the importance of preserving the urban effect in weather files for building studies. The generated climate dataset can be used as the input of EnergyPlus building simulations to evaluate the spatial-temporal pattern of indoor overheating. The validated climate model can also be extended for the future projection of urban scale overheating studies. In the future, overheating mitigation strategies can be applied to the baseline model to evaluate their effectiveness on both historical and future climate in the long run with the proposed workflow and the climate dataset generated by this study.
... Examples of empirical indices include the Effective Temperature (ET), Wet Bulb Globe Temperature (WBGT), and others. Rational indices include physiological parameters such as sweat rate, core temperature, or heart rate and parameters that are calculated based on the physiological models [22,23], such as Predicted Heat strain (PHS), Standard Effective Temperature (SET), and others. Summertime building thermal performance is not only related to indoor temperature but also other environmental parameters, as well as occupants' physiological responses [2]. ...
... The rational heat-stress index considers both multiindoor environment parameters and occupants' physiological responses, which is therefore a suitable indicator of building thermal performance. The Standard Effective Temeprature (SET) is a widely used rational heat-stress index based on the two-node physiological model [22] and is also recommended by ASHRAE-55 [16] to evaluate indoor thermal conditions. SET is defined as the air temperature of an imaginary standard environment where the relative humidity is 50%, air speed <0.1 m/s, and mean radiant temperature equals to air temperature, and the total heat loss from an imaginary occupant with an activity level of 1.0 met, and a clothing level of 0.6 clo is the same as that from a person in the actual environment with actual clothing and activity level [16]. ...
... The resilience trapezoid model is used in this study to suit the characteristic variation in building thermal performance during heatwave events as observed from measurements in real buildings. Fig. 2 shows the Standard Effective Temperature (SET) (black curve) in a building calculated based on a two-node bioheat model [22] by inputting measuring the indoor temperature and relative humidity from May to August [35] and the local outdoor air temperature (grey curve) measured from the roof of the building. With 28 • C as the threshold temperature for indoor (CIBSE TM59 [12]) and outdoor (Canadian guideline [36]) heat events, the red and green shading highlights the time frames during which indoor overheating and outdoor heatwave events occur, respectively. ...
Quantifying building resilience to extreme weather conditions helps identify the capability of a building system to tolerate disturbances and recover from extreme events. The robustness of building retrofit strategies can also be evaluated through their contributions to building resilience. In this study, building thermal resilience to summertime heatwaves is defined based on the concept of resilience trapezoid. The Thermal Resilience Index (TRI) with several labeling classes (Class F to Class A+) is proposed to quantify the resilience levels with respect to the relative improvement from original indoor thermal conditions. In addition to evaluating the overall resilience of a building, the resilience of each thermal zone in the building can be quantified with the proposed TRI criteria. A quantification framework is proposed by using the Standard Effective Temperature (SET) index as the performance indicator, and the entire procedure is demonstrated with a long-term care building of five stories. Four retrofit measures and their combinations are implemented to improve the building resilience to heatwaves. The results show layered multiple strategies are necessary to improve both the overall resilience of the building and the resilience of its component thermal zones. The resilience of the building can achieve the level of Class B after the combined strategies are applied, providing an improvement of 50%–70% in the degree of resilience. The proposed TRI index and spatial distribution analysis are useful in evaluating the overall and zonal resilience of a building.
... The development of the bioheat model for older people is built on a newly developed two-node model for young adults (Ji et al. 2021). The age-related physiological changes of older people are accounted for in the thermoregulatory actions and threshold values triggering these actions. ...
... For young people, the values of T cr0,dil and T sk0,cons are 36.8 °C and 33.7 °C, respectively (Ji et al. 2021). For older people, T cr0,dil is higher and T sk0,cons is lower, resulting in delays in triggering vasodilation and vasoconstriction. ...
... Under hot and humid exposure conditions, sweating evaporation is restricted, resulting in a decrease in sweat evaporation efficiency (Itani et al. 2020b). The model of Kubota et al. (2014) for the sweat evaporation efficiency was found to produce better results than other formulations (Ji et al. 2021), and therefore, it is adopted in this study. The sweat evaporative efficiency is given by Eq. (9). ...
Physiological modeling is important to evaluate the effects of heat and cold conditions on people’s thermal comfort and health. Experimental studies have found that older people (above 65 year old) undergo age-related weakening changes in their physiology and thermoregulatory activities, which makes them more vulnerable to heat or cold exposure than average aged young adults. However, addressing the age-related changes by modeling has been challenging due to their wide variability among the older population. This study develops a two-node physiological model to predict the thermal response of older people. The model is built on a newly developed two-node model for average-age young adults by accounting for the age-related attenuation of thermoregulation and sensory delays in triggering thermoregulatory actions. A numerical optimization method is developed to compute the model parameter values based on selected benchmark data from the literature. The proposed model is further validated with published measurement data covering large input ranges. The model predictions are in good agreement with the measurements in hot and cold exposure conditions with a discrepancy 0.60 °C for the mean skin temperature and of 0.30 °C for the core temperature. The proposed model can be integrated into building simulation tools to predict heat and cold stress levels and the associated thermal comfort for older people in built environments.
... This is accomplished by the autonomic nervous system's control of thermoregulatory activities, including sweating, shivering, vasoconstriction, and vasodilation. The activities are controlled by both active coefficients (to determine the intensity of the thermoregulatory activities) and set point temperatures (to trigger the thermoregulatory activities and determine intensity) [33]. Most of the prevailing thermoregulation models are established for non-elderly adults (aged <60) and further incorporate reduced functions in thermoregulation activities and physiological changes in elderly people [30]. ...
... As a result, the target function should first be determined to evaluate model performance. Root mean square error (RMSE) has been a frequently used evaluation method for thermoregulation models [33,54,75,76]. RMSE quantifies model performance using the differences between predicted and measured values. ...
Given the increasing aging population and rising living standards in China, developing an accurate and straightforward thermoregulation model for the elderly has become increasingly essential. To address this need, an existing one-segment four-node thermoregulation model for the young was selected as the base model. This study developed the base model considering age-related physical and physiological changes to predict mean skin temperatures of the elderly. Measured data for model optimization were collected from 24 representative healthy Chinese elderly individuals (average age: 67 years). The subjects underwent temperature step changes between neutral and warm conditions with a temperature range of 25–34 °C. The model's demographic representation was first validated by comparing the subjects' physical characteristics with Chinese census data. Secondly, sensitivity analysis was performed to investigate the influences of passive system parameters on skin and core temperatures and adjustments were implemented using measurement or literature data specific to the Chinese elderly. Thirdly, the active system was modified by resetting the body temperature set points. The active parameters to control thermoregulation activities were further optimized using the TPE (Tree-structured Parzen Estimator) hyperparameter tuning method. The model's accuracy was further verified using independent experimental data for a temperature range of 18–34 °C for Chinese elderly. By comprehensively considering age-induced thermal response changes, the proposed model has potential applications in designing and optimizing thermal management systems in buildings, as well as informing energy-efficient strategies tailored to the specific needs of the Chinese elderly population.
... A long-term care building model is simulated with all the weather data. Older people and young people's bioheat models [4] [5] are used to evaluate the occupants' thermal response to the indoor conditions. The retrofitted scenarios of the building model are also evaluated with the occupants' bioheat model. ...
... The model can be used to predict occupants' physiological responses such as core temperature and skin temperature, as well as heat-stress indices such as Standard Effective Temperature (SET). To compare with the traditional method that uses a general young adult's model to evaluate occupants' thermal responses, the young people's bioheat model [4] is also used to predict young occupants' thermal responses and compare them with older people. ...
This study explores the spatial variability of the thermal responses of older occupants’ in indoor environments at various locations in Montreal, Canada. Urban climate in and around the city over an extreme heat event in 2018 is simulated at 1 km spatial resolution using Weather Research and Forecasting (WRF) model, and data for 29 long-term care building locations in the city is extracted. A long-term care building was modelled using EnergyPlus and calibrated with measured hourly indoor air temperatures; it was then used to evaluate how local weather conditions affect indoor thermal conditions. The building simulation results were then applied to a physiological model for older (65y+) people that permitted evaluating individual's thermal responses. Further analysis was conducted by comparing the difference between responses of older and young occupants and between people’s responses in original and retrofitted buildings. More than 4 ℃ variations in the standard effective temperature (SET) of older occupants are observed in the studied buildings. The conclusions highlight the importance of applying strict indoor temperature limiting thresholds or retrofit requirements for long-term care buildings.KeywordsOlder peopleThermal riskLong-term care buildingMicroclimate
... Body dehydration and core temperature should be calculated using suitable physiological models of the human body. In this work, the two-node bioheat models of Ji et al. [70,71] for average young and older adults were used. The above limit criteria (Equation (5)) depend on building type, occupant's vulnerability to heat, and local climate. ...
... The overheating limit criteria were obtained by correlating the duration (DUR), severity (SETH), and intensity (INT) of overheating events to the aforementioned cumulative body water loss (dehydration) and maximum body core temperature. Body dehydration and core temperature were calculated using simplified two-node bioheat models for young and older people [70,71]. Overheating is declared if any of the three limit criteria is exceeded in any given space or entire building space. ...
Climate heat waves occurring in urban centers are a serious threat to public health and wellbeing. Historically, most heat-related mortalities have arisen from excessive overheating of building interiors housing older occupants. This paper developed an approach that combines the results from building simulation and bioheat models to generate health-based limit criteria for overheating in long-term care homes (LTCHs) by which the body dehydration and core temperature of older residents are capped during overheating events. The models of the LTCHs were created for buildings representative of old and current construction practices for selected Canadian locations. The models were calibrated using measurements of indoor temperature and humidity acquired from monitoring the building interiors and the use of published building energy use intensity data. A general procedure to identify overheating events and quantify their attributes in terms of duration, intensity, and severity was developed and applied to LTCHs to generate the limit criteria. Comparing the limit criteria from the proposed and comfort-based methods showed evident differences. The proposed method predicted the overheating risk consistent with the overall thermal comfort during overheating events in contrast to the comfort-based methods. The new limit criteria are intended to be used in any study to evaluate overheating risk in similar buildings.
... where Q r is the ratio of the respiratory heat exchange to the total metabolic rate, γ is a coefficient equal to one if the internal heat generation (M C ) is provided by the body metabolism (M C > 0) or zero if the internal heat is provided by an external source (M C ≤ 0). Similarly, the mechanical efficiency (η) will be zero if M C ≤ 0. The NST heat at the comfort state (M nstC ) is calculated as a function of the deviations of the core and mean skin temperatures at the comfort state from their neutral values at the resting state using a suitable shivering model (e.g., model of Ji et al. [37]; or Tikuisis and Giesbrecht [38]). The ratio Q r is derived from Equation (6) as follows: In most cases (with M C > 0), the required net internal heat (M C ) to achieve a comfort state under the imposed environmental conditions comes from the metabolic rate of activity performed under such comfort conditions. ...
... Adopting the two-node physiological models for young and older people of Ji et al. [37,40], and neglecting the rate of change in the skin evaporative diffusion and dry heat compared to the NST heat and evaporative sweating, Equation (19) reduces to: ...
The PMV index forms the basis of international thermal comfort standards. PMV was developed based on empirical relationships between the metabolic rate of activity and the body mean skin temperature and evaporative heat loss under the comfort conditions. However, many recent studies have questioned the accuracy and reliability of the PMV predictions, particularly for the discomfort range. This paper develops a general formulation of PMV that does not involve the mean skin temperature and evaporative heat loss. The new metabolic-based predicted mean vote (MPMV) index is expressed as the difference between the metabolic rate of activity and the metabolic rate required to achieve a comfort state under the imposed environment conditions. The comfort metabolic rate is found to vary linearly with the metabolic rate required to maintain the body core and mean skin temperatures at the resting thermo-neutral state. The model constants are determined using public experimental data on thermal sensation votes of young and older people. The new formulation accounts for body core cooling to achieve comfort under hot exposures; it also addresses the overlooked non-shivering thermogenesis in the body heat balance at the comfort state and covers comfort requirements for young and older people in wakeful and sleep states.
... A long-term care building model is simulated with all the weather data. Older people and young people's bioheat models [4] [5] are used to evaluate the occupants' thermal response to the indoor conditions. The retrofitted scenarios of the building model are also evaluated with the occupants' bioheat model. ...
... The model can be used to predict occupants' physiological responses such as core temperature and skin temperature, as well as heat-stress indices such as Standard Effective Temperature (SET). To compare with the traditional method that uses a general young adult's model to evaluate occupants' thermal responses, the young people's bioheat model [4] is also used to predict young occupants' thermal responses and compare them with older people. ...
This study explores the spatial variability of the thermal responses of older occupants' in indoor environments at various locations in Montreal, Canada. Urban climate in and around the city over an extreme heat event in 2018 is simulated at 1 km spatial resolution using Weather Research and Forecasting (WRF) model, and data for 29 long-term care building locations in the city is extracted. A long-term care building was modelled using EnergyPlus and calibrated with measured hourly indoor air temperatures; it was then used to evaluate how local weather conditions affect indoor thermal conditions. The building simulation results were then applied to a physiological model for older (65y+) people that permitted evaluating individual's thermal responses. Further analysis was conducted by comparing the difference between responses of older and young occupants and between people’s responses in original and retrofitted buildings. More than 4℃ variations in the standard effective temperature (SET) of older occupants are observed in the studied buildings. The conclusions highlight the importance of applying strict indoor temperature limiting thresholds or retrofit requirements for long-term care buildings.
... In our study, we observed wider limits of agreement for core temperature measurements compared to typical clinical settings [16,30,41,50]. This difference can be attributed to the less abrupt and drastic changes in core temperature observed in clinical cases as well as that these models are validated under well controlled conditions. ...
We previously developed the FAME Lab PHS software (PHSFL), a free offline software to calculate the predicted heat strain for a group of individuals based on the ISO 7933. The objectives of this study were to: upgrade the PHSFL from an offline (desktop-version) tool to a web-based platform, as well as assess its validity in recreational athletes in different forms of exercise and across various temperature recording methodologies and environmental conditions. The web PHSFL was developed as browser-based software developed using HTML, CSS, and JavaScript, and included several updates from the previous offline version. Its validity was assessed in 83 healthy non-smoking males and females during rest, exercise, and post-exercise recovery in 165 trials (cycling: 97; running: 68). Trials were performed in an environmental chamber under varying environmental conditions: 19.1 to 40.6 °C air temperature, 30.0% to 60.0% relative humidity, 0.1 to 0.5 m/s wind speed, and 0 or 800 W/m2 solar radiation. Comparison of actual vs. predicted core body temperature showed 0.85 Willmott’s Index of Agreement, 0.76 (P < 0.001) correlation coefficient, and 95% limits of agreement of 0.16 ± 0.83 °C (mean difference ± 95% limits). Results for rectal temperature showed 0.79 Willmott’s Index of Agreement, 0.68 (P < 0.001) correlation coefficient, and 95% limits of agreement of 0.18 ± 0.76 °C. Results for skin temperature showed 0.77 Willmott’s Index of Agreement, 0.75 (P < 0.001) correlation coefficient, and 95% limits of agreement of − 0.24 ± 2.28 °C. We conclude that the web PHSFL provides acceptably accurate predictions of core body temperature and skin temperature to be used as indicators of physiological heat strain.
... Enfin, Ji et al. étudient le modèle de Gagge et sa sensibilité à divers modèles de thermorégulation pour les comparer à la donnée mesurée afin de proposer une mise à jour du modèle à deux noeuds. En revanche, ils ne s'intéressent pas à la sensibilité aux entrées du modèle (Ji et al. 2021). ...
Pilotage des systèmes en rafraichissement mixte centré sur le confort: une étude de sensibilité du modèle de Gagge pour une application en climat tropical. RESUME. Les bâtiments en rafraichissement mixte (MM : Mixed-mode) offrent la flexibilité nécessaire pour assurer à la fois, le confort et la réduction de la consommation d'énergie dans les climats tropicaux, en combinant simultanément plusieurs dispositifs de refroidissement tels que la climatisation et les ventilateurs de plafond. Pour les concepteurs, il est nécessaire de contrôler simultanément ces derniers pour évaluer performance énergétique et de confort dans la simulation énergétique du bâtiment (BEM : Building Energy Model), et ainsi, dimensionner les systèmes couplés en conséquence. Non seulement la température doit être contrôlée, mais la vitesse et la vêture s'ajoutent désormais à la liste. Après une revue des modèles de confort les plus courants, des techniques d'analyse de sensibilité sont utilisées pour évaluer l'avantage du contrôle des paramètres environnementaux sur la variabilité de sortie du modèle de confort du bilan thermique, en particulier la SET (Standard Effective Temperature) du modèle de Gagge. Cela donne un premier aperçu des stratégies de contrôle des systèmes pour atteindre le confort dans différentes conditions intérieures chaudes d'été tropical.
MOTS-CLÉS : Confort thermique, sensibilité de modèles, stratégies de pilotage
... Ji. L. et al. explored and validated the accuracy of a variety of thermal models and provided some recommendations for overheating buildings [42,43]. These works have contributed to the development of thermal comfort research. ...
A large number of people in China still live in rural villages. The indoor environment of these rural dwellings directly affects the quality of life of the occupants. Nevertheless, constrained by the quality of dwelling construction, rural buildings have poorer indoor environments and, at the same time, have a higher operating energy consumption. However, inadequate attention has been given to the summer thermal environment in cold regions. This work has been carried out around the thermal environment of rural residences in cold regions during summer. Field measurements, questionnaires, and data analysis were used in this study. We recorded the indoor and outdoor thermal environment parameters on a typical summer day in the Linyi rural area. Moreover, the subjective sensations and thermal adaptive behaviors of the participants were recorded in detail with a questionnaire. Linear regression showed that the neutral temperature for residents in summer was 27.52 °C, with acceptable temperatures ranging from 25.14 °C to 29.9 °C. Age and gender differences were found to affect the occupants’ sensation of thermal comfort and humidity, as well as their thermal adaptive behavior. In addition, a thermal adaptive model has been constructed in the study, which will further enrich the thermal adaptive investigation and provide a scientifically sound reference for the renovation and development of the local rural areas.
... Our previous work on overheating in residential buildings found that the severity and duration attributes correlate well with the cumulative body dehydration level, and the intensity attribute correlates well with the maximum body core temperature [60]. Body dehydration and core temperature are calculated using the two-node physiological model for young adults of Ji et al. [78]. ...
Overheating in school buildings can negatively affect the cognitive learning performance of particularly young students whose thermoregulation systems are still developing. However, currently, in schools, issues related to overheating have been addressed by limiting the exposure time to thermal discomfort. In this paper, the development of a general procedure that combines building and bioheat simulations to evaluate overheating risk in schools and generate health-based overheating limit criteria that may be applied in Canadian schools is described. General school building models, having either old or new constructions, were created based on a primary school building and successfully calibrated using field measurements of indoor temperature and humidity and published building energy use intensity data. Three sets of two limit criteria (exposure duration and severity of overheating) that account for the personal exposure conditions of students in primary, middle, and secondary schools were developed by limiting the body dehydration of students during extreme overheating events. Comparing the proposed limit criteria with the hour of exceedance criterion revealed interesting relationships between them, suggesting the proposed limit criteria as a benchmark for the comfort-based criteria, particularly for the more vulnerable primary and middle schools. The proposed procedure with the obtained overheating limit criteria is intended to be applied in any field or simulation study to assess the risk of overheating in similar school buildings under any local prevailing climate.
... Laouadi et a. [4] summarized existing overheating criteria and found most of them are using temperature-based metrics. A bio-heat thermal index, standard effective temperature (SET), is therefore proposed for evaluation of overheating in Canada [4], [5]. However, there are many other thermal indices that can evaluate thermal conditions in buildings, while there is still a lack of study comparing different thermal indices. ...
In this study, a comparison is made of overheating in multiple field-monitored buildings using several different overheating indices, including dry-bulb temperature, the heat index (HI), humidex (H), standard effective temperature (SET), wet-bulb globe temperature (WBGT), discomfort index (DI), and summer simmer index (SSI). The field monitoring was conducted in the city of Montreal, Canada over the summer of 2020 at six school buildings and two hospital buildings. In at least two typical rooms of each building, temperature and humidity sensors were installed; a total of 34 rooms were instrumented in these 8 buildings, and the rooms are facing different orientations and are located on different floors. The extent of concordance amongst the different overheating metrics was examined by correlation analysis. The result from this study provides an evaluation of the similarity between the different assessment metrics and helps identify the assessment approach that is the most representative of the methods evaluated.KeywordsField monitoringOverheatingCorrelation analysisHeat indexStandard effective temperatureHumidex
... S.23 and S.26), the air humidity during the heatwave was significantly increased than pre-and post-heatwave conditions, which is consistent with other findings (Ngarambe et al., 2020;Pyrgou et al., 2020) whereby extreme hot spells are normally associated with high air humidity. The increased air humidity can significantly worsen heat stress by impairing the thermoregulatory system (Ji et al., 2021), which in turn increases morbidity and mortality during the event. ...
In this study, the synergistic interactions of the urban heat island effect and the heatwaves occurring in the summer of 2018 in the cities of Montreal and Ottawa, Canada, are discussed through a comparison between days with and without a heatwave event. Three (3) time frames were prepared as part of this comparison: a pre-heatwave period from June 24 to June 29, 2018 (6 days); a heatwave period from June 30 to July 05, 2018 (6 days); and a post-heatwave period from July 06 to July 11, 2018 (6 days). The urban climates of these two cities were simulated using a Weather Research and Forecast (WRF) model at a grid resolution of 1 km; the urban heat island intensity was calculated using two methods: (i) calculating the temperature difference between urban and rural grid cells at a distance ranging between 3 to 10 km to the boundary of the urban area; (ii) using the “urban increment” method by which the temperature of urban grid cells are compared to the results of another simulation having all the urban land cover replaced by cropland. The diurnal evolution of several near-surface variables was compared throughout these three periods, including the ground surface temperature, the 2-m air temperature, relative humidity, and the 10-m wind speed.KeywordsUrban heat islandWeather Research and Forecast (WRF)HeatwaveExtreme heat eventHeat wave-UHI synergy
... Heat exchange takes place between core part and skin part, of them, the value of heat conduction can reflect indivisul difference; compared with peers with normal body fat rate, obese people have lower skin temperature and thermal sensation vote in the same cold environment (Ooijen et al., 2006), when they have similar core temperature. Furthermore, in hot environment, skin vasodilation leads to an increase in blood flow from core to skin, resulting in higher skin temperature and larger heat dissipation (Ji et al., 2021); therefore, the regression coefficient of skin temperature-ambient temperature is not equal to 1 (Zhu et al., 2017), due to the fact that skin temperature is actually affected by both external thermal environment and internal thermal regulation. Although the core temperature may not change significantly with ambient temperature, it is essential for the mechanism analysis of individual differences in thermal perceptions. ...
Body temperature serves as the principal factor in thermal perception determination. Current thermal comfort researches mainly focused on skin temperature, while other kinds of body temperatures were often ignored. In laboratory with strictly controlled environment, 26 subjects (13 males and 13 females) remained seated for a duration of 130 minutes in two thermal environments (19°C and 35°C), arranged in a particular order; four kinds of body temperatures (skin temperature, oral temperature, auditory canal temperature and breath temperature) and three kinds of thermal perception votes (thermal sensation, thermal comfort and thermal acceptable) were regularly collected. The analysis results showed that, skin temperature and breath temperature significantly changed with ambient temperature (p < 0.001); the difference between average value of core temperature in two conditions was small (≤ 0.3°C), but a significant difference was almost observed in auditory canal temperature of males (p = 0.07). Both skin temperature and breath temperature were significantly related with three subjective votes (p < 0.001), meanwhile, the prediction accuracy of breath temperature for thermal perception was in no way inferior to skin temperature. Although oral temperature and auditory canal temperature had partial significant correlations with thermal perception, they were difficult to be carried out in practical application due to their weak explanatory powers (correlation coefficient < 0.3). In summary, this research tried to establish correlation laws between body temperatures and thermal perception votes during a temperature step-change experiment, while finding the potential of utilizing breath temperature for thermal perception prediction, which is expected to be further promoted in the future.
... S.23 and S.26), the air humidity during the heatwave was significantly increased than pre-and post-heatwave conditions, which is consistent with other findings (Ngarambe et al., 2020;Pyrgou et al., 2020) whereby extreme hot spells are normally associated with high air humidity. The increased air humidity can significantly worsen heat stress by impairing the thermoregulatory system (Ji et al., 2021), which in turn increases morbidity and mortality during the event. ...
The heatwave event to which the Ontario-Quebec area was subjected in 2018 resulted in significant morbidity and mortality. In this study, an investigation was conducted on how this heatwave affected the respective urban heat islands (UHIs) in Montreal and Ottawa. The modelled urban climates were compared before, during and after the heatwave using a Weather Research and Forecast (WRF) model having a 1 km spatial resolution. The UHI was calculated using two methods. As a first method, the “rural-ring” method was used to calculate the UHI in regard to temperature differences between urban and surrounding rural areas. The second method used the “urban-increment” approach where simulation results were compared to another simulation in which urban cells are replaced by croplands. Results show that urban land can raise temperatures by up to 12 °C at surfaces and 6 °C in the near-surface air. A synoptic anticyclone in the lower atmosphere was responsible for the heatwave, although both cities were located in areas peripheral to the anticyclone. During the heatwave, precipitation at the initial stage of the event and low wind conditions largely varied the pattern of the UHI effect within each urban center. The UHI was generally unchanged or even reduced during this heatwave, but there was substantial diurnal variation. Around noon and in the afternoon, the UHI was amplified by up to 3 °C, whereas it was suppressed or even negative at sunrise.
... In the muscle layer, the blood flow rate is the sum of basal heat flow and blood flow induced by working and shivering [28,37]. ...
A multi-segmented human bioheat model was developed to characterize the heat exchange phenomena of the human body while immersed in water and to predict the body's thermoregulation and physiological reactions under immersed conditions. The model is based on the Tanabe model, which divides the human body into 15 segments according to the “Walter” thermal manikin. Each segment is made up of the core layer, muscle layer, fat layer, skin layer and central blood layer. The wet clothing model, which uses empirical equations derived from several underwater manikin experiments, is used in the multi-segmented human bioheat model to represent the mechanism of heat exchange between the human body and the outside world through wet clothing after falling into water. Ten healthy males were chosen for the water immersion experiment and placed in a climatic chamber with simulated falling water. Six different body parts' core temperatures, skin temperatures, and heat flow densities were continually monitored throughout the experiment. By contrasting the experimental results of the water immersion environment with the multi-segmented human bioheat model's predicted outcomes, the accuracy of the model was assessed. The results showed that the values predicted by the model simulation matched the results of the experiments, which indicates that the multi-segmented human bioheat model was reliable, valid, and very useful.
... Laouadi et a. [4] summarized existing overheating criteria and found most of them are using temperature-based metrics. A bio-heat thermal index, standard effective temperature (SET), is therefore proposed for evaluation of overheating in Canada [4], [5]. However, there are many other thermal indices that can evaluate thermal conditions in buildings, while there is still a lack of study comparing different thermal indices. ...
In this study, a comparison is made of overheating in multiple field-monitored buildings using several different overheating indices, including dry-bulb temperature, the heat index (HI), humidex (H), standard effective temperature (SET), wet-bulb globe temperature (WBGT), discomfort index (DI), and summer simmer index (SSI). The field monitoring was conducted in the city of Montreal, Canada over the summer of 2020 at six school buildings and two hospital buildings. In at least two typical rooms of each building, temperature and humidity sensors were installed; a total of 34 rooms were instrumented in these 8 buildings, and the rooms are facing to different orientations and are located on different floors. The extent of concordance amongst the different overheating
metrics was examined by correlation analysis. The result from this study provides an evaluation of the similarity between the different assessment metrics and helps identify the assessment approach that is the most representative among the methods evaluated
Urban Green Infrastructure (UGI) plays a critical role in creating Thermal Resilient Communities (TRCs) by mitigating the adverse climate change impacts and enhancing thermal resilience. In this paper, we consider a range of UGI types, from street trees, urban parks, green spaces, and building-integrated UGI components, such as green roofs and green walls. It is intended that the thermal/aerodynamic effects of UGI be analyzed and the influence of UGI at three spatial scales within urban communities be provided; more specifically: microclimate scale, building scale, and individual scale. The study discusses the findings of 218 papers published since 2010 to elaborate on recent progress in this field. This review delves into three fundamental aspects related to UGI: a) the underlying mechanisms allowing UGI to affect the environment and human health, b) the modeling efforts undertaken to simulate these effects, and c) measurement studies providing empirical data and verifying the models. Additionally, the effects of UGI across the three scales and related aspects are discussed, and the importance of integrative mechanisms and coupled modeling approaches is emphasized. Key methodologies for integrating microclimate and building simulations are summarized, highlighting the data exchange protocols and software tools. In contrast to previous reviews, this study has analyzed major measurement studies, categorizing them into full-scale and sub-scale studies. As a conclusion, several potential directions for future research are identified, including advanced physical and data driven UGI models, enhanced coupled simulations for indoor-outdoor interactions, establishment of measurement datasets for benchmarking both UGI modeling and coupled simulations.
Radiant systems have the potential to save energy and thermal comfort. Currently, many researchers have studied the effect of thermal comfort in radiant asymmetrical environments. However, there are few focuses on the thermal comfort of the elderly. This study aims to investigate the effect of radiant asymmetry caused by the positions and radiant temperature of the radiant panel on elderly’s thermal comfort. The view factor between the radiant panel and the human body was determined using a ray-tracing method and combined with the physiological model to assess the thermal comfort of the human body. The findings revealed that ceiling radiation provided the best thermal comfort. The radiant panel’s temperature affects the skin temperature, standard effective temperature (SET), and thermal sensation vote (TSV). The elderly have a higher core temperature (0.22 °C) and lower skin temperature (0.26 °C) compared with the younger. This work can guide the radiant systems operation and terminal design.KeywordsRadiant coolingRadiant characterThermal comfortThermal sensationRay tracing
The interactions between climate change and urbanization have generated mounting concerns regarding outdoor and indoor overheating within urban populations, impacting thermal comfort, heat-related mortality, and energy consumption, particularly during heat waves that are becoming more frequent, intense, and prolonged. Various strategies have been proposed to mitigate overheating in urban environments among the past few years. To effectively examine the impacts of overheating and evaluate mitigation strategies in both current and future climates, it is first necessary to produce reliable climate projections that can accurately describe the state of the urban climate; which is a complex system comprising of unique microclimate phenomena connected to regional and global. This paper presents a systematic review of the application of climate model projections for future indoor and outdoor overheating impact assessments, divided into four primary stages: (1) Mesoscale raw future climate data generation using GCM-RCMs; (2) Local-scale future climate input preparation through bias-correction and reference year data generation; (3) Microscale indoor and outdoor simulations with building performance models or computational fluid dynamics (CFD) software; (4) Overheating evaluation based on various overheating criteria. These stages are essential for advancing our understanding of overheating and informing future studies in this area. With the target keywords of above four stages, the methodology applied to identify and select articles from search results as suitable candidates was shown in Section 2. Key research gaps illustrated by this review include challenges in generating climate data, improving projected data reliability, and addressing indoor/outdoor climate simulation complexities. Additionally, incorporating social-economic factors into overheating evaluation methods is crucial for a comprehensive assessment. Although the focus is future urban overheating assessment, the general methodologies and procedure of future climate projections may also apply to other building performance simulations considering the climate change impacts. Notable research gaps were then identified as avenues for future research.
Selecting appropriate evaluation indices to evaluate extremely hot environments has become an urgent problem to ensure the safety of workers’ lives and improve labor productivity. The objective of this review is to compare relevant evaluation indices for assessing heat stress and to analyze the more effective ones applicable to the industrial environment by considering each one together. Based on extensive research on existing literature and relevant international standards, this paper introduces the definitions of heat stress and its influencing factors. Based on the actual needs of workers, analyze the existing heat stress indices from a practical standpoint, and summarize the definitions, advantages, and limitations of each index. As a result, this paper emphasizes that the relevant thresholds of thermal indices should be appropriately adjusted to suit different industrial environments. The hazards of extreme work environments can be reduced by developing new indices, charts, or related strategies. This review will provide a solid theoretical foundation for environmental control schemes for harsh working situations.
The accurate prediction of thermal comfort in indoor environment is an important precondition for energy-saving in buildings. In this study, a thermal comfort evaluation model for buildings with solar Trombe wall, by incorporating multiple thermal comfort indicators, i.e. the average values of temperature difference in the air near the head and feet (Δt), predicted mean vote (PMV), predicted percentage of dissatisfied (PPD) and standard effective temperature (SET), was proposed and validated. As the application of this proposed model, the indoor thermal comfort of a single room with two Trombe walls was evaluated and compared. Also, the indoor thermal comfort under different ambient conditions was discussed. The results show that the proposed model can efficiently realize the management of indoor thermal comfort, and is meaningful for energy-saving design of modern buildings and promoting the application of Trombe wall. There exist differences in the time periods occupied by different thermal comfort states for different evaluation indicators, which will lead to different building energy-saving effects under the condition of maintaining comfortable indoor environment. The SET indicator has stricter requirements for comfort state, and is more suitable for changeable indoor environment of the room with solar Trombe wall. Ambient conditions have significant effects on the indoor thermal comfort. Solar Trombe wall can create a very comfortable indoor environment for warmer regions in heating seasons, and provide a cool and uncomfortable environment when the ambient temperature is lower than 9 ℃ and the global solar radiation intensity is smaller than 200 W/m².
Purpose:
Time-weighted averaging is used in occupational heat stress guidelines to estimate the metabolic demands of variable-intensity work. However, compared to constant-intensity work of the same time-weighted average metabolic rate, variable-intensity work may cause decrements in total heat loss (dry + evaporative heat loss) that exacerbate heat storage in women. We therefore used direct calorimetry to assess whole-body total heat loss and heat storage (metabolic heat production minus total heat loss) in women and men during constant- and variable-intensity work of equal average intensity.
Methods:
Ten women [mean (SD); 31 (11) years] and fourteen men [30 (8) years] completed two trials involving 90-min of constant- and variable-intensity work (cycling) eliciting an average metabolic heat production of ~ 200 W/m2 in dry-heat (40 °C, ~ 15% relative humidity). External work was fixed at ~ 40 W/m2 for constant-intensity work, and alternated between ~ 15 and ~ 60 W/m2 (5-min each) for variable-intensity work.
Results:
When expressed as a time-weighted average over each work period, total heat loss did not differ between men and women (mean difference [95% CI]; 4 W/m2 [- 11, 20]; p = 0.572) or between constant- and variable-intensity work (1 W/m2 [- 3, 5]; p = 0.642). Consequently, heat storage did not differ significantly between men and women (- 4 W/m2 [- 17, 8]; p = 0.468) or between constant- and variable-intensity work (0 W/m2 [- 3, 3]; p = 0.834).
Conclusion:
Neither whole-body heat loss nor heat storage was modulated by the partitioning of work intensity, indicating that time-weighted averaging is appropriate for estimating metabolic demand to assess occupational heat stress in women.
The Center for the Built Environment (CBE) Thermal Comfort Tool is a free online tool for thermal comfort calculations and visualizations that complies with the ASHRAE 55–2017, ISO 7730:2005 and EN 16798–1:2019 Standards. It incorporates the major thermal comfort models, including the Predicted Mean Vote (PMV), Standard Effective Temperature (SET), adaptive models, local discomfort models, SolarCal, and dynamic predictive clothing insulation. Our tool also provides dynamic and interactive visualizations of thermal comfort zones. The CBE Thermal Comfort Tool has several practical applications and each year is used by more than 49,000 users worldwide, including engineers, architects, researchers, educators, facility managers and policymakers.
Thermal adaptations mitigate thermal discomfort and thereby reducing building energy consumption. Adaptive-rational thermal comfort modelling modifies the rational model using the adaptive approach to enable the rational model to fully account for the thermal adaptations for improved thermal comfort prediction. However, the applicability of the adaptive-rational thermal comfort modelling has only been verified for the rational model of Predicted Mean Vote. Standard effective temperature (SET) is a more advanced rational model that has been adopted by ASHRAE 55. This study proposes two SET based adaptive-rational thermal comfort models. Predicted thermal sensation (PTS) is obtained from the SET. The PTS is modified by the extension method and adaptive method of the adaptive-rational thermal modelling to be the extended PTS and adaptive PTS respectively. Case studies using field data from naturally-ventilated and air-conditioned buildings validate that, compared with the PTS, the conventional extended PTS and conventional adaptive PTS improve the accuracy in the thermal sensation prediction by 6.6–36.5%, and the proposed extended PTS and proposed adaptive PTS improve the accuracy by 29.4–77.0%. Thus, the proposed extended PTS and proposed adaptive PTS significantly outperform the conventional ones. Besides the improved accuracy, the proposed extended PTS and proposed adaptive PTS are explicitly formulated and convenient for practical applications.
Overheating in buildings arising from climatic extreme heat events has been identified as a health concern to vulnerable occupants. However, there have been very limited studies to generate suitable weather data to evaluate by simulation the overheating risk and its effect on the comfort and health of occupants. This paper develops a methodology to identify reference summer weather years (RSWY) for overheating risk analysis. The methodology includes generation of historical climate data, and development of a heat
stress metric for the definition and characterization of heat events. The Standard Effective Temperature was selected among a short list of popular metrics, modified and named t-SET to account for transient heat events, activity levels of occupants, and thermoregulatory controls of sleeping subjects. The t-SET model predictions compared well with measured body temperatures of subjects undergoing multi-stage activities
under hot conditions. The t-SET index was used to generate RSWY for selected Canadian cities.
The assessment of thermal sensation is the first stage of many studies aimed at addressing thermal comfort and at establishing the related criteria used in indoor and outdoor environments. The study of thermal sensation requires suitable modelling of the human body, taking into account the factors that affect the physiological and psychological reactions that occur under different environmental conditions. These aspects are becoming more and more relevant in the present context in which thermal sensation and thermal comfort are represented as objectives or constraints in a wider range of problems referring to the living environment. This paper first considers the models of the human body used in steady-state and transient conditions. Starting from the conceptual formulations of the heat balance equations, this paper follows the evolution occurred during the years to refine the models. This evolution is also marked by the availability of increasingly higher computational capability that enabled the researchers developing transient models with a growing level of detail and accuracy, and by the validation of the models through experimental studies that exploit advanced technologies. The paper then provides an overview of the indicators used to characterise the local and overall thermal sensation, indicating the relations with local and overall thermal comfort.
https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.3120
Ageing plays a fundamental role in arterial blood transport and heat transfer within a human body. The aim of this work is to provide a comprehensive methodology, based on biomechanical considerations, for modelling arterial flow and energy exchange mechanisms in the body accounting for age‐induced changes. The study outlines a framework for age‐related modifications within several interlinked subsystems, which include: arterial stiffening, heart contractility variations, tissue volume and property changes, and thermoregulatory system deterioration. Some of the proposed age‐dependent governing equations are directly extrapolated from experimental datasets. The computational framework is demonstrated through numerical experiments, which show the impact of such age‐related changes on arterial blood pressure, local temperature distribution, and global body thermal response. The proposed numerical experiments show that the age‐related changes in arterial convection do not significantly affect the tissue temperature distribution. Results also highlight age‐related effects on the sweating mechanism, which lead to a significant reduction in heat dissipation and a subsequent rise in skin and core temperatures.
Rising environmental temperatures represent a major threat to human health. The activation of heat advisories using evidence-based thresholds for high-risk outdoor ambient temperatures have been shown to be an effective strategy to save lives during hot weather. However, although the relationship between weather and human health has been widely defined by outdoor temperature, corresponding increases in indoor temperature during heat events can also be harmful to health especially in vulnerable populations. In this review, we discuss our current understanding of the relationship between outdoor temperature and human health and examine how human health can also be adversely influenced by high indoor temperatures during heat events. Our assessment of the existing literature revealed a high degree of variability in what can be considered an acceptable indoor temperature because there are differences in how different groups of people may respond physiologically and behaviorally to the same living environment. Finally, we demonstrate that both non-physiological (e.g., geographical location, urban density, building design) and physiological (e.g., sex, age, fitness, state of health) factors must be considered when defining an indoor temperature threshold for preserving human health in a warming global climate.
The transient nature of the occupant heat load is not fully addressed and implemented in a building simulation tool. In this paper, the effect of using dynamic occupant heat loads in building simulation on energy building performance and occupant thermal comfort has been studied. A two-node thermoregulatory model was integrated into ESP-r. The predictions of the integrated two-node model were compared to two commonly used approaches in building simulation: gains modelled as a basic fixed profile and gains modelled using a polynomial function of temperature and relative humidity. The variation in occupant thermal load demonstrated appreciable differences on both cooling and dehumidification loads.
Direct calorimetry is the gold standard means of measuring human metabolic rate and its use has been fundamental for understanding metabolism in health and disease. While metabolic rate is now more commonly estimated indirectly from measures of the oxygen consumed during respiration, direct calorimetry provides the user with the unique capacity to quantify the heat produced from aerobic and anaerobic metabolism by measuring heat exchange between the body and the environment. This review provides a brief historical overview of the fundamental concepts which underlie direct calorimetry, of pioneer scientists which developed these concepts into functional pieces of equipment and the subsequent use of direct calorimetry to advance our understanding of energy balance, nutrition, and the pathogenesis of metabolic diseases. Attention is directed to seminal studies that successfully employed direct calorimetry to verify that the law of energy conservation also applies to human beings and to establish the validity of indirect calorimetry. Finally, we discuss the more recent use of direct calorimetry for the measurement of whole-body heat exchange and body heat storage in the study of human thermoregulation.
https://link.springer.com/article/10.1007/s00421-017-3670-5
The human body’s heat exchange and its interaction with the surrounding environment has in the past years been the research focus of a number of disciplines. As a result, a number of human thermoregulation models have been developed since the first was developed in 1970. The aim of this paper is to conduct a review of existing thermophysiological models for the whole body and isolated body segments. The course of the development from simple to more complex models is shown, and most recognized thermal models such as Fiala, Berkeley Comfort Model, Tanabe, and ThermoSem model are concisely described. Furthermore, possible applications of the models in various research disciplines are introduced. In the built environment, the developed models are used as part of the methodology for modelling thermal comfort in buildings.
The assessment of the risk of human exposure to heat is a topic as relevant today as a century
ago. The introduction and use of heat stress indices and models to predict and quantify heat stress
and heat strain has helped to reduce morbidity and mortality in industrial, military, sports, and
leisure activities dramatically. Models used range from simple instruments that attempt to mimic
the human-environment heat exchange to complex thermophysiological models that simulate both
internal and external heat and mass transfer, including related processes through (protective) clothing.
This article discusses the most commonly used indices and models and looks at how these
are deployed in the different contexts of industrial, military, and biometeorological applications,
with focus on use to predict related thermal sensations, acute risk of heat illness, and epidemiological
analysis of morbidity and mortality. A critical assessment is made of tendencies to use
simple indices such as WBGT in more complex conditions (e.g., while wearing protective clothing),
or when employed in conjunction with inappropriate sensors. Regarding the more complex
thermophysiological models, the article discusses more recent developments including model individualization
approaches and advanced systems that combine simulation models with (body
worn) sensors to provide real-time risk assessment. The models discussed in the article range from
historical indices to recent developments in using thermophysiological models in (bio) meteorological
applications as an indicator of the combined effect of outdoor weather settings on humans.
© 2016 American Physiological Society. Compr Physiol 6:255-302, 2016.
Heat waves are the cause of many preventable deaths around the world, especially among older adults and in countries with more temperate climates. In the present study, we examined the effects of age on whole-body heat loss and heat storage during passive exposure to environmental conditions representative of the upper temperature extremes experienced in Canada. Direct and indirect calorimetry measured whole-body evaporative heat loss and dry heat exchange, as well as the change in body heat content. Twelve younger (21 ± 3 years) and 12 older (65 ± 5 years) adults with similar body weight (younger: 72.0 ± 4.4 kg; older: 80.1 ± 4.2 kg) and body surface area (younger: 1.8 ± 0.1 m²; older: 2.0 ± 0.1 m²) rested for 2 h in a hot–dry [36.5 °C, 20% relative humidity (RH)] or hot–humid (36.5 °C, 60% RH) environment. In both conditions, evaporative heat loss was not significantly different between groups (dry: p = 0.758; humid: p = 0.814). However, the rate of dry heat gain was significantly greater (by approx. 10 W) for older adults relative to younger adults during the hot–dry (p = 0.032) and hot–humid exposure (p = 0.019). Consequently, the cumulative change in body heat content after 2 h of rest was significantly greater in older adults in the hot–dry (older: 212 ± 25 kJ; younger: 131 ± 27 kJ, p = 0.018) as well as the hot–humid condition (older: 426 ± 37 kJ; younger: 317 ± 45 kJ, p = 0.037). These findings demonstrate that older individuals store more heat during short exposures to dry and humid heat, suggesting that they may experience increased levels of thermal strain in such conditions than people of younger age.
Over the past century more than 100 indices have been developed and used to assess bioclimatic conditions for human beings. The majority of these indices are used sporadically or for specific purposes. Some are based on generalized results of measurements (wind chill, cooling power, wet bulb temperature) and some on the empirically observed reactions of the human body to thermal stress (physiological strain, effective temperature). Those indices that are based on human heat balance considerations are referred to as "rational indices". Several simple human heat balance models are known and are used in research and practice. This paper presents a comparative analysis of the newly developed Universal Thermal Climate Index (UTCI), and some of the more prevalent thermal indices. The analysis is based on three groups of data: global data-set, synoptic datasets from Europe, and local scale data from special measurement campaigns of COST Action 730. We found the present indices to express bioclimatic conditions reasonably only under specific meteorological situations, while the UTCI represents specific climates, weather, and locations much better. Furthermore, similar to the human body, the UTCI is very sensitive to changes in ambient stimuli: temperature, solar radiation, wind and humidity. UTCI depicts temporal variability of thermal conditions better than other indices. The UTCI scale is able to express even slight differences in the intensity of meteorological stimuli.
The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.
Prediction formulae of shivering metabolism (Mshiv) are critical to the development of models of thermoregulation for cold exposure, especially when the extrapolation of survival times is required. Many such formulae, however, have been calibrated with data that are limited in their range of core temperatures (Tc), seldom involving values of less than 36 degrees C. Certain recent studies of cold-water immersion have reported Tc as low as 33.25 degrees C. These data comprise measurements of Tc (esophageal) and mean skin temperature (Ts), and metabolism from 14 males [mean (SD); age = 28 (5) years; height = 1.78 (0.06) m; body mass = 77.7 (6.9) kg; body fat (BF) = 18.4 (4.5)%] during immersion in water as cold as 8 degrees C for up to 1 h and subsequent self-rewarming via shivering under dry blanketed conditions. The data contain 3343 observations with mean (SD) Tc and Ts of 35.92 (0.93) degrees C and 23.4 (8.9) degrees C, respectively, and have been used to re-examine the prediction of Mshiv. Rates of changes of these temperatures were not used in the analysis. The best fit of the formulae, which are essentially algebraic constructs with and without setpoints, are those with a quadratic expression involving Ts. This is consistent with the findings of Benzinger (1969) who demonstrated that the thermosensitivity of skin is parabolic downwards with temperature peaking near a value of 20 degrees C. Formulae that included a multiplicative interaction term between Tc and Ts did not predict as well. The best prediction using 37 degrees C and 33 degrees C as the Tc and Ts setpoints, respectively, was found with BF as an attenuation factor: Mshiv (W x m(-2)) = [155.5 x (37- Tc) + 47.0 x (33 - Ts) - 1.57 x (33 - Ts)2]/(%BF)(0.5).
Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database,
we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macrotrend of how people feel thermally under different temperatures. Three types of factors which might lead to different
thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 oC; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 oC. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 oC wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort
researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models.
Two mathematical models of human thermal regulation include the rational Predicted Heat Strain (PHS) and the thermophysiological model by Fiala. The approaches of the models are different, however, they both aim at providing predictions of the thermophysiological responses to thermal environments of an average person. The aim of this study was to compare and analyze predictions of the two models against experimental data. The analysis also includes a gender comparison. The experimental data comprised of ten participants (5 males, 5 females, average anthropometric values were used as input) conducting an intermittent protocol of rotating tasks (cycling, stacking, stepping and arm crank) of moderate metabolic activities (134–291 W/m²) with breaks in-between in a controlled environmental condition (34 °C, 60% RH). The validation consisted of the predictions’ comparison against experimental data from 2.5 hours of data of rectal temperature and mean skin temperature based on contact thermometry from four body locations. The PHS model over-predicted rectal temperatures during the first activity for males and the cooling effectiveness of sweat in the recovery periods, for both males and females. As a result, the PHS simulation underestimated the thermal strain in this context. The Fiala model accurately predicted the rectal temperature throughout the exposure. The fluctuation of the experimental mean skin temperature was not reflected in any of the models. However, the PHS simulation model showed better agreement than the Fiala model. As both models predicted responses more accurately for males than females, we suggest that in future development of the models it is important to take this result into account. The paper further discusses possible sources of the observed discrepancies and concludes with some suggestions for modifications.
Thermoregulation models of the human body have been widely used in thermal comfort studies. The existing models are complicated and not fully verified for application in China. This paper presents a simplified thermoregulation model which has been statistically validated by the predicted and measured mean skin temperature in warm environments, including 21 typical conditions with 400 Chinese subjects. This model comprises three parts: i) the physical model; ii) the controlled system; and iii) the controlling system, and considers three key questions formerly ignored by the existing models including: a) the evaporation efficiency of regulatory sweat; b) the proportional relation of total skin blood flow and total heat loss by regulatory sweating against body surface area; and c) discrepancies in the mean skin temperatures by gender. The developed model has been validated to be within the 95% confidence interval of the population mean skin temperature in three cases.
Individualized model of human thermoregulation for the simulation of heat stress response. J Appl Physiol 90: 1943-1954, 2001. - A population-based dynamic model of human thermoregulation was expanded with control equations incorporating the individual person's characteristics (body surface area, mass, fat%, maximal O2 uptake, acclimation). These affect both the passive (heat capacity, insulation) and active systems (sweating and skin blood flow function). Model parameters were estimated from literature data. Other data, collected for the study of individual differences {working at relative or absolute workloads in hot-dry [45°C, 20% relative humidity (rh)], warm-humid [35°C, 80% rh], and cool [21°C, 50% rh] environments}, were used for validation. The individualized model provides an improved prediction [mean core temperature error, -0.21 → -0.07°C (P < 0.001); mean squared error, 0.40 → 0.16°C, (P < 0.001)]. The magnitude of improvement varies substantially with the climate and work type. Relative to an empirical multiple-regression model derived from these specific data sets, the analytical simulation model has between 54 and 89% of its predictive power, except for the cool climate, in which this ratio is zero. In conclusion, individualization of the model allows improved prediction of heat strain, although a substantial error remains.
This study discusses the difference in temperature elevation and sweating between younger and older adults during ambient heat exposure. The bioheat equation is solved computationally in an anatomically based human body model to track the variation in the temperature and sweating in the time domain. Our computational code is improved by introducing different blood temperatures in different body regions and taking into account the maximum possible evaporative heat loss. The reduced thermal sensitivity of the hypothalamus in the aged adults (mean age of 73.9 years) is also estimated from literature data and taken into account in a revised formula for the thermoregulatory response. For ambient heat exposure (a temperature of 40 °C and relative humidity of 42%), our computational results are in good agreement with measurement data in the literature for both younger adults (mean age of 23.5 years) and the elderly (67.8 years old), suggesting the effectiveness of our improved bioheat modeling. The reduction in the thermal sensitivity of the hypothalamus is estimated as 0.6 ± 0.2 °C for the aged (mean age of 73.9 years), although it was not significant for the elderly (67.8 years). For an ambient temperature of 35 °C and relative humidity of 60%, the computed core temperature elevation in the model corresponding to the thermophysiological response of the aged is 0.92 °C, which is higher than those for the younger adults, 0.25 °C, and for the elderly, 0.45 °C. This difference in the core temperature elevation is attributable mainly to the decline in the thermal sensitivity of the hypothalamus. The total perspiration at ages of 67.8 years and 73.9 years was 904 g and 645 g, respectively, which is smaller than that of the younger adults, 1090 g.
The arterio-venous anastomoses (AVA) play a major role in the blood circulation in the peripheral body parts. In this work, the segmental bioheat model of Salloum et al. [1] is improved to accurately predict skin blood flow rate in the hands and fingers, and the local and overall human thermal responses in transient environments. The improvements in the model include: 1) extending the artery tree to include the arterial branching to the five fingers; 2) modeling and distribution of the blood flow between the deep and superficial veins in the peripherals; 3) adjusting arteries' radii during dilation and constriction; 4) innovative modeling of AVA of the fingers. The model focus is on the accurate blood flow calculation to the different body segments proposing a better blood control mechanism through relating the arterial tree radii as well as the AVA control mechanism to cardiac output. The skin blood flow and digits' dynamic thermal response predicted by the model were compared with published experimental values on body core and skin temperatures and local skin temperatures of fingers. Good agreement was obtained with experimentally reported values on average skin, core, and finger skin temperature response of subjects exposed to gradual decrease in air temperature from 32.3 °C to 13 °C. The new integrated AVA model of the fingers with the bioheat model is capable of predicting digits' dynamics thermal response with better accuracy than some previous models while also incorporating the complex central and local thermoregulatory functions.
A bioheat model for the elderly was developed focusing on blood flow circulatory changes that influence their thermal response in warm and cold environments to predict skin and core temperatures for different segments of the body especially the fingers. The young adult model of Karaki et al. (Int J Therm Sci 67:41-51, 2013) was modified by incorporation of the physiological thermoregulatory and vasomotor changes based on literature observations of physiological changes in the elderly compared to young adults such as lower metabolism and vasoconstriction diminished ability, skin blood flow and its minimum and maximum values, the sweating values, skin fat thickness, as well as the change in threshold parameter related to core or skin temperatures which triggers thermoregulatory action for sweating, maximum dilatation, and maximum constriction. The developed model was validated with published experimental data for elderly exposure to transient and steady hot and cold environments. Predicted finger skin temperature, mean skin temperature, and core temperature were in agreement with published experimental data at a maximum error less than 0.5 °C in the mean skin temperature. The elderly bioheat model showed an increase in finger skin temperature and a decrease in core temperature in cold exposure while it showed a decrease in finger skin temperature and an increase in core temperature in hot exposure.
This paper applies the heat balance equation (HBE) for clothed subjects as a linear function of mean skin temperature (t sk ) by a new sweating efficiency (η sw ) and an approximation for the thermoregulatory sweat rate. The equation predicting t sk in steady state conditions was derived as the solution of the HBE and used for a predictive heat strain scale. The heat loss from the wet clothing (WCL) area was identified with a new variable of 'virtual dripping sweat rate VDSR' (S wdr ). This is a subject's un-evaporated sweat rate in dry clothing from the regional sweat rate exceeding the maximum evaporative capacity, and adds the moisture to the clothing, reducing the intrinsic clothing insulation. The S wdr allowed a mass balance analysis of the wet clothing area identified as clothing wetness (w cl ). The w cl was derived by combining the HBE at the WCL surface from which the evaporation rate and skin heat loss from WCL region are given. Experimental results on eight young male subjects wearing typical summer clothing, T-shirt and trousers verified the model for predicting t sk with WCL thermal resistance (R cl,w ) identified as 25 % of dry clothing (R cl,d ).
Thermoregulation and sleep interacted each other and thermoregulatory responses were present during sleep to maintain small oscillations of core temperature of a sleeping person. In order to improve sleep quality, it was necessary to investigate the thermoregulatory responses of a sleeping person. This paper reports on a study of developing a four-node thermoregulation model for predicting thermal physiological responses of a sleeping person, based on the existing Gagge’s two-node model. Firstly, the Gagge’s two-node model is introduced in detail. This is followed by reporting the necessary modifications to Gagge’s two-node model for developing the four-node thermoregulation model for a sleeping person. Thirdly, the four-node thermoregulation model is presented in detail. Lastly, the validation of the four-node thermoregulation model by comparing the predicted thermal physiological responses, including skin and core temperatures, using the model developed with the experimental data previously obtained by others is presented. The comparison results showed that the four-node thermoregulation model developed could be used to predict the thermal physiological responses of a sleeping person, with an acceptable accuracy.
The validity of the two-node model for predicting the skin temperature in the thermal steady state is studied by comparing the calculated and experimental results for various thermal conditions. For the experimental results of steady-state skin temperature, in addition to the authors’ original experimental data, literature data for mean skin temperature are collected, incorporating 56 conditions and 233 subjects in total. The results show that the two-node model (the 1986 edition) that is widely used for calculating SET* predicts effectively the steady-state skin temperature in the low-activity conditions. Additionally, the changes that were made to the two-node model by Gagge et al. and ASHRAE are summarized. It is shown theoretically and by experimental validations that, of these changes, the addition of the shivering model represents the most significant improvement in terms of predicting the skin temperature in the steady state.
Temperature and sensory indicates of human response to the thermal environment are often expressed in terms of the known response in a controlled laboratory environment, as a standard. The three rational indices of this type to be considered are ASHRAE's Standard Effective Temperature (SET*) Index, defined as the equivalent dry bulb temperature of an isothermal environment at 50% RH in which a subject, while wearing clothing standardized for activity concerned, would have the same heat stress (skin temperature T/sub sk/) and thermo-regulatory strain (skin wettedness, w) as in the actual test environment; Fanger's Predicted Mean Vote (PMV) Index, defined in terms of the heat load that would be required to restore a state of ''Comfort'' and evaluated by his Comfort Equation; and Winslow's Skin Wettedness Index of ''Thermal Discomfort'' (DISC) defined in terms of the fraction of the body surface, wet with perspiration, required to regulate body temperature by evaporative cooling.
To develop a thermal model that can predict the thermal responses of the human body under given environmental conditions, it is necessary for the model to be fitted with the individual characteristics of human body temperature regulation. As the basis for this, in this paper, it is shown that the coefficients that represent the thermoregulatory responses in the two-node model (thermal model of human body) can be identified for individuals. Six coefficients related to the regulation of sweating and skin blood flow in the two-node model are tuned for the individuals involved in the experiments—the core and skin temperatures calculated by the model are fitted with the measured results for the entire thermal transient processes, including exposures to heat and cold.
Rates of evaporation and sweating were recorded for three acclimatized male subjects in hot humid conditions, the ambient parameters of which were set so that the various imposed evaporative rates required the same skin wettedness at different levels of sweating. Rectal and skin temperatures were measured. Results showed that during steady state occurring during the 2nd h of exposure each subject reached the required evaporative rate by means of increases in skin wettedness regardless of the level of sweating; the sweat evaporative efficiency, defined as the ratio between evaporative rate and sweat rate, decreased as skin wettedness increased, in a range between 0.74 and 1.0 Sweat efficiency fell to 0.67 for fully wet skin. The body temperatures did not increase with time if skin wettedness was less than unity. Evaporative heat transfer coefficient (he), maximum evaporative capacity, and wettedness were estimated on the basis of the observed decrease of sweat efficiency. The relationship between skin wettedness and sweat efficiency was interpreted as a combined effect of differences in local he as well as in local sweat rates.
Nine young (20-25 years) and ten older (60-71 years) men, matched for body fatness and surface area:mass ratio, underwent cold tests in summer and winter. The cold tests consisted of a 60-min exposure, wearing only swimming trunks, to an air temperature of 17 degrees C (both seasons) and 12 degrees C (winter only). Rectal (Tre) and mean skin (Tsk) temperatures, metabolic heat production (M), systolic (BPs) and diastolic (BPd) blood pressures and heart rate (fc) were measured. During the equilibrium period (28 degrees C air temperature) there were no age-related differences in Tre, Tsk, BPs, BPd, or fc regardless of season, although M of the older men was significantly lower (P < 0.003). The decrease in Tre and Tsk (due to the marked decrease in six of the older men) and the increase in BPs and BPd were significantly greater (P < 0.004) for the older men during all the cold exposures. The rate of increase in M was significantly greater (P < 0.01) for the older group when exposed to 12 degrees C in winter and 17 degrees C in summer (due to the marked increase in four of the older men). This trend was not apparent during the 17 degrees C exposure in winter. There was no age-related difference in fc during the exposures. Significant decreases in Tre and Tsk and increases in M, BPs and BPd during the 12 degrees C exposure were observed for the older group (P < 0.003) compared to their responses during the 17 degrees C exposure in winter. In contrast, Tre, M, BPs in the young group were not affected as much by the colder environment. It was concluded that older men have more variable responses and some appear more or less responsive to mild and moderate cold air than young men.
A mathematical model for predicting human thermal and regulatory responses in cold, cool, neutral, warm, and hot environments has been developed and validated. The multi-segmental passive system, which models the dynamic heat transport within the body and the heat exchange between body parts and the environment, is discussed elsewhere. This paper is concerned with the development of the active system, which simulates the regulatory responses of shivering, sweating, and peripheral vasomotion of unacclimatised subjects. Following a comprehensive literature review, 26 independent experiments were selected that were designed to provoke each of these responses in different circumstances. Regression analysis revealed that skin and head core temperature affect regulatory responses in a nonlinear fashion. A further signal, i.e. the rate of change of the mean skin temperature weighted by the skin temperature error signal, was identified as governing the dynamics of thermoregulatory processes in the cold. Verification and validation work was carried out using experimental data obtained from 90 exposures covering a range of steady and transient ambient temperatures between 5 degrees C and 50 degrees C and exercise intensities between 46 W/m2 and 600 W/m2. Good general agreement with measured data was obtained for regulatory responses, internal temperatures, and the mean and local skin temperatures of unacclimatised humans for the whole spectrum of climatic conditions and for different activity levels.
Thermal comfort and thermoregulation for elderly people taking light exercise
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Comparison of the proposed model predictions with the experimental data for the core and skin temperatures under cold exposure of scenario 9
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Fig. 16. Comparison of the proposed model predictions with the experimental data
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