Clothing Insulation Research Articles

Investigating Thermal Comfort Indices in Relation to Clothing Insulation Value: A survey of an outdoor space in Tehran, Iran

Investigating Thermal Comfort Indices in Relation to Clothing Insulation Value: A survey of an outdoor space in Tehran, Iran

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

Field Investigation on the Thermal Environment and Comfort of People Exercising in a Fitness Center

A favorable thermal environment in fitness centers is important to attract more members and is beneficial to the health of exercising people. The purpose of this study was to research the actual thermal environment of a typical fitness center in different seasons and the thermal requirement of exercising people. A field investigation covering winter, spring, and summer was conducted. The environmental parameters were measured. Subjective questionnaires involving individual information, clothing insulation, thermal sensation, etc., were collected. Participants’ heart rates were tested to estimate their metabolic rate (MR). A total of 740 valid questionnaires were collected. The results showed that a scissors gap existed between the predictive mean vote (PMV) and the thermal sensation vote (TSV) for the exercising people. For the higher MR group, there was a separation between the TSV and thermal preference vote, e.g., most participants would not prefer to cooler or warmer thermal environment when they felt hot or cold. The CO2 concentration changed greatly among seasons and the distribution in the fitness center was not uniform. With mechanical ventilation, the CO2 concentration in summer was the lowest. In other seasons it became much higher due to limited natural ventilation. However, subjective response to indoor air quality showed no significant difference among seasons. The participants felt more satisfied to the overall thermal environment in the transition season. The results can be referenced in the thermal environment management in fitness centers during seasonal changes.

Mesh Refinement for Dynamics Airflow in Health Care

This studyexplores using computational fluid dynamics(CFD) tooptimize ventilation in healthcare, with a focus on mesh refinement for accurate airflow analysis. The goal is to reduce airborne contaminant spread, which is crucial during COVID-19. Ventilation in healthcare ensures air quality and minimizes pathogen transmission. The research analyzer analyzes mesh element size's impact on airflow accuracy in simulated hospital wards with two coughing patients. The optimal mesh size is determined for reliable predictions. Mesh refinement enhances accuracy in critical zones. The k-epsilon turbulence model is chosen for low error. Experimental validation shows temperature discrepancies, likely due to simulated manikins lacking clothing insulation. Air velocity measurements show minor variations within an acceptable range. Further research on various ventilation configurations and airflow rates in real hospital conditions is crucial. Addressing these limitations optimizer optimizeshealthcare ventilation, enhancing safety and infection control.

Thermal perception and dressing behaviors of pregnant women during different stages of pregnancy: A field study at a hospital in Xi'an, China

Pregnant women have different thermal preferences during pregnancy. This paper aims to investigate the thermal characteristics of pregnant women in hospitals, addressing a research gap in the field of thermal comfort for this population. This study assessed the thermal environments of the pregnancy examination spaces (i.e., B-ultrasound room and waiting area) in a hospital located in Xi'an during the winter season. Data were collected from 1055 pregnant women at different stages of pregnancy, including demographic characteristics, thermal sensation votes (TSVs), thermal comfort votes (TCVs), and clothing insulation. The results show that the thermal sensation was significantly more influenced by the pregnancy stage than the current weight. Pregnant women in the third trimester reported a higher mean thermal sensation vote (MTSV) than in other pregnancy stages. Furthermore, the clothing insulation of pregnant women exhibited a non-normal distribution, resembling a bimodal structure. Based on this structure, typical clothing insulation values were determined to be 0.9 clo in the B-ultrasound room and 1.3 clo in the waiting area. Given these characteristics in both thermal perception and dressing behaviors of pregnant women via a subjective questionnaire, we advise lowering the temperature to ensure their comfort and save heating energy. Some personalized seats are also provided to compensate for differences in thermal preference at various stages of pregnancy. These findings contribute to the creation of comfortable and healthy pregnancy examination environments.

Self-assessed threshold for cold temperatures and thermal insulation of clothing among poultry workers

The association between self-assessed cold threshold (CT) and thermal insulation of clothing (Icl) was analysed in 283 poultry workers in Thailand. The mean CT was 13.5 °C (range − 28–29) and the mean Icl was 1.23 clo (range 0.35–2.21). The adjusted CT remained unchanged at low Icls (0.35 through 1.25 clo) but was estimated to increase by 14.8 °C at high Icls (1.25 through 2.21 clo). Overall, CT was higher by 2.4 °C (95% confidence interval [CI] 0.3–3.8) at high (≥ 1.25 clo) than that at low (< 1.25 clo) Icl, but this difference was modified by personal and work-related factors. The difference was 2.6 °C (CI 0.5–4.6) for older (30–57 y) compared to younger (18–29 y) participants, with an excess of 7.3 °C (CI 5.6–9.0) for low vs high educated participants, 2.6 °C (CI 0.5–4.8) for those doing heavy vs light work, 7.4 °C (CI 3.7–11.0) for alcohol consumers vs others, and 3.4 °C (CI 0.6–6.3) for smokers vs non-smokers. The differences were independent of personal characteristics and worksite physical conditions and were interpreted as increased cold sensitivity among subgroups with lesser stamina and poorer health. Sensitive worker subgroups should be identified, and their need for cold protection should be reviewed.

Evaluating the influence of ambient conditions in the cooking space of railway pantry car using selected thermal indices and physiological parameter.

Hot and humid indoor environment of the kitchen affects worker performance. The Indian Railway's pantry car culinary is one of them that cooks food for the on-board passengers, which could be bothered by the hot indoor climate. The current study aimed to identify the indoor working environment of the railway "pantry car" using heat stress indices such as "Universal Thermal Climate Index-UTCI," "Wet-Bulb Globe Temperature-WBGT," "Discomfort Index-DI," "Tropical Summer Index-TSI," "Heat Index-HI," and Heart Rate-HR with clothing insulation. METHODS: The study was performed in 2018 (August-summer season) to collect field survey data on 6 railway pantry cars. Measurements were carried out during peak cooking times such as morning "7 : 00 am", day "11 : 30 am", evening "4 : 00 pm," and night "6 : 30 pm". This study's descriptive and Pearson's correlation analysis was accomplished using SPSS version 2016 software. The analysis results revealed that the average values were for UTCI (37.77±5.26°C), WBGT (30.42±2.28°C), DI (30.05±2.70°C), TSI (33.21±2.90°C), HI (48.53±4.86°C), correspondingly. During analysis, the strongest correlation association was observed between "TSI and DI" (r = 0.985, p < 0.000) and WBGT and TSI (r = 0.958, p < 0.000). A "significant correlation" was found between UTCI and HI (r = 0.637, p < 0.05). While no signification correlation was found between "heat stress indices and physiological parameters (p > 0.05)". In this study, all the heat stress index limit values showed highly harsh working conditions inside the pantry car, which created unfavorable circumstances for the culinarians. Inappropriate "ventilation design" could be a reason for discomfort in the railway pantry car.

Dynamic energy optimization strategy to provide appropriate thermal comfort and fresh air under occupants’ different activity types

An effective strategy for optimizing indoor air distribution in stratum ventilation heating systems under different types of human activity has been proposed, in order to provide appropriate thermal comfort while exploiting the potential for energy savings. The stratum ventilation was optimized using strategy 1 (the optimization strategy of three ventilation performance indicators based on VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)), the strategy 2 (the optimization strategy with constraint based on VIKOR) and the strategy 3 (the optimization strategy aimed at energy conservation), respectively. These strategies provide optimal indoor air distribution within a stratum ventilation system that is influenced by various factors (i.e. supply air temperature, supply air vane angle, supply air velocity, metabolic rate, clothing insulation and outdoor air temperature). Firstly, the parameters of the stratum ventilation system are obtained using Computational Fluid Dynamics (CFD) software. In addition, using the simulation results as input variables, predictive models of ventilation performance (i.e., PMV, local mean air age (LMAA) and energy consumption) was developed using Artificial Neural Network (ANN). Finally, by optimizing the proposed ANN based strategy 1, strategy 2 and strategy 3, the ventilation performance is output. The proposed strategy 1 provides a comfortable indoor environment and saves energy, and compared to the strategy 1, the strategy 3 saves more energy while ensuring that thermal comfort meets the requirements. In addition, considering the optimization of clothing insulation while providing appropriate thermal comfort also indirectly saves energy. Moreover, these strategies also work well as the outdoor air temperature changes.

Comparison and correction on calculation methods of clothing insulation based on thermal comfort

Clothing insulation is a major factor affecting human thermal comfort, and it is usually a fixed value when the clothing ensemble are determined in the standard. However, real clothing insulation is considerably affected by the environment and activity level (metabolic rate). Thus, the heat flow method for obtaining clothing insulation through thermal equilibrium between the skin layer and environment was proposed. Later, a non-contact method for easy acquisition of clothing insulation was proposed. The method was implemented via infrared imager. In this paper, above methods were compared and analysed based on experiment data. Three activity levels were considered, sedentary (1.0 met), walking at speed of 0.8 m/s (1.8 met), and 1.2 m/s (2.6 met). The feasibility and accuracy of above methods are discussed by comparing the PMV calculated by the clothing insulation with the actual thermal sensation (TSV) of the subjects obtained by questionnaire. The results showed that the non-contact method exhibited the highest prediction accuracy. Due to the sweating effect on skin temperature, the higher the activity levels, the lower the prediction accuracy. In this paper, a correction method of clothing insulation was proposed by using the temperature of face and neck and skin wettedness. After correction, the root means square error between the PMV and TSV decreased by 11 %.

Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning

Various individual thermal preferences do not always lead to optimal thermal comfort, despite the high energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. Thermal comfort models often struggle to accurately predict occupants’ thermal sensations due to their inability to adapt to diverse scenarios. This research employs machine learning techniques to develop personalized data-driven thermal comfort models using a global database of 17,814 samples from the ASHRAE Global Thermal Comfort Database II. Specifically, the study implements random forest and k-nearest neighbor algorithms to analyze the influence of environmental factors (such as air temperature, outdoor temperature, relative humidity, and air velocity) and personal factors (including clothing insulation, activity level, and individual differences) on thermal comfort perception. The study systematically compares the predictive performance of different output types (thermal sensation vs. preference vote), input parameters (6 vs. 12), and machine learning methods. The optimized random forest model with 12 inputs demonstrated an accuracy of over 70% in predicting thermal preference votes, which was significantly higher than the 34% accuracy of the PMV model. The results present new optimized data-driven models and offer insights into the relative influence of parameters such as climate, building cooling strategies, and occupant age and gender. This research aims to enable personalized thermal comfort predictions to improve satisfaction and energy efficiency in various buildings.

Thermoregulatory differences in sex and time-of-day within naturally ventilated office environments

Thermal comfort research lacks continuous data collection on thermoregulation and adaptive behavior in real-world environments. This paper presents the findings of a field study that aimed at understanding the temporal and individual differences between humans’ thermoregulation and adaptation within a dynamic thermal environment. The dataset contains a 6-week long summer field study of 2022 observing nine participants in a naturally ventilated office without a cooling system. The data collection comprises a total of 43 measurement days (22,776 data points at 60s frequency). The results indicate that the overall trend in clothing insulation deviates from standard assumptions (0.5 clo), especially above air temperatures of 26 °C. The mean clothing insulation was 0.29 clo ±0.08, overall ranging from 0.20 to 0.73 clo. The data showed significant sex differences in tonic levels of electrodermal activity, the low-frequency to high-frequency heart rate variability ratio, and clothing insulation. Other heart rate variability data, like the root mean square of successive differences and beats per minute, as well as the distal skin temperature of the wrist, show significant sex differences and time-of-day variations in morning-to-afternoon comparisons. The findings indicate a nuanced relationship between air temperature and thermoregulatory responses, highlighting the need for future research with larger, more diverse participant cohorts and enhanced environmental monitoring to strengthen the validity and generalizability of the findings.

Humidity-Responsive Liquid Metal Core-Shell Materials for Enduring Heat Retention and Insulation.

High humidity in extremely cold weather can undermine the insulation capability of the clothing, imposing serious life risks. Current clothing insulation technologies have inherent deficiencies in terms of insulation efficiency and humidity adaptability. Here, humidity-stimulated self-heating clothing using aluminum core-liquid metal shell microparticles (Al@LM-MPs) as the filler is reported. Al@LM-MPs exhibit a distinctive capability to react to water molecules in the air to generate heat, exhibiting remarkable sensitivity across a broad temperature range. This ability leads to the creation of intelligent clothing capable of autonomously responding to extreme cold and wet weather conditions, providing both enduring heat retention and insulation capabilities.

Variations in Gender Perceptions of Summer Comfort and Adaptation in Colonial Revival-Style Homes

Past investigations have assessed gender variations in thermal comfort and adaptation in different buildings. However, no reported study has evaluated differences in gender perceptions of the thermal environment in US Colonial Revival-style homes. As a result, this study aims to provide an understanding of variations in gender perceptions of summer comfort and adaptation in the buildings. The study evaluated data collected during the field studies of thermal comfort (FSTC), including physical measurements of environmental variables and subjects’ votes. In this study, 67% females and 33% males participated in the field surveys in summertime. The mean neutral temperature of 25.5 °C and preferred temperature of 24.9 °C were noted for females. For males, the mean neutral and preferred temperatures of 25.8 °C and 25.6 °C were observed. Females feel neutral and prefer “no change” to the thermal environment at lower temperatures than males. A difference of 0.3 °C was noted between the average heat indices for females and males. Male respondents who feel warm perceive less air quality more than females. Females and males who perceive being thermally comfortable also perceive good air quality. Females and males who are thermally comfortable rated air humidity to be acceptable. There are similarities and differences between gender perceptions of the thermal environment. The investigation suggests interventions that can help users regulate their skin temperatures and use control measures that are sensitive to clothing insulation. The research also calls for policies that can promote building users’ pivotal adaptive measures to improve their indoor thermal environment. The study enhances our understanding of sustainable indoor environments and how to improve living conditions and adaptive modifications among different groups of occupants in buildings.

Overheating in the Tree Shade of Urban Parks: A Field Study of Thermal Adaption in China

With increased atmospheric temperature, temperatures in the shade of trees in parks also increase, and people are faced with high temperature challenges. In this study, thermal comfort in the shade of the trees of an urban park during summer in China was assessed. The subjective responses of the respondents were recorded via questionnaires, and environment parameters were measured. The results show that the air temperature in the shade was 31.1 ± 3.0 °C during the day, and that it peaked at 36.9 °C; the globe temperature was 31.3 ± 3.1 °C, and it peaked at 40.1 ℃. Respondents’ clothing insulation was 0.31 ± 0.08 clo, and the effect of clothing adjustment on thermal adaptation was limited. Thermal sensation is linearly related to standard effective temperature (SET), and the upper limit of 80% acceptable SET was 32.1 °C. At different temperature values, the proportion of expected airflow enhancement exceeded 50%. The respondents preferred a neutral-warm sensation. Moreover, there was an obvious thermal adaptation, with thermal history and psychological adaptation being the main factors affecting thermal comfort. This study confirmed the value of shade and provided us with guidance for park planning and design.

Development of adaptive behaviour models for thermal comfort: Longitudinal investigation in Japanese office buildings and literature review

In the recent year's energy consumptions has increased, especially in the office buildings for heating and cooling use. Adaptive behaviours such as clothing adjustments, window opening, fan use, heating use and cooling use are important contributing factors for thermal comfort of the occupants. The main objectives of this study is to clarify different behavioural adaptations carried out by occupants in Japanese office buildings and to compare with previous studies. The occupant behaviour models were developed for clothing adjustments and air-conditioning use. Occupant behaviour survey was conducted in the five mixed mode (MM) and one heating, ventilation, and air-conditioning (HVAC) Japanese office buildings over a period of fifteen months resulting in 16,411 votes from 45 participants. Range of clothing insulation was wider in MM buildings compared to HVAC buildings which indicate that occupant behaviour in MM buildings required less energy for thermal comfort. The mean proportion of window opening and fan use was 0.54 and 0.41 respectively. Developed logistic model suggested that 80% of occupant used heating and cooling when the outdoor air temperature drops below 11 °C and rise above 27 °C respectively. The obtained results from field investigation are validated by the extensive literature review. The occupant behaviour models in Japanese office buildings are comparable with the behaviours models from different parts of the world having various climatic conditions. It was also confirmed that occupants behaviours are effective for energy saving building designs. The acknowledgement of these behaviour of the occupants is fruitful in designing thermally comfortable and possibly energy saving MM buildings in future.

The Study of Thermal Comfort in a Common Commercial Car Cabin: Looking at Malaysia’s Perspective

Assessing thermal comfort in vehicles poses unique challenges compared to buildings. The in-car environment is characterized by specific factors such as sunlight exposure, poor clothing insulation, non-uniform average radiant temperature, and limited time to establish comfort parameters. To address these challenges, this study aims to evaluate thermal comfort in different branded car cabins by analyzing temperature distribution, air velocity, relative humidity, and dew point temperature. A comprehensive review of literature was conducted, focusing on various aspects including airflow regimes, factors influencing thermal comfort, measuring instruments, and comfort indices. Understanding these factors is crucial for developing comprehensive methodologies to assess thermal comfort and indoor air quality in vehicle cabins. The results of the study indicate that MPV cars offer superior comfort levels within the optimal range of -1 to +1, surpassing sedans and compact cars. These findings are instrumental in guiding design and engineering decisions to improve passenger comfort in vehicles. Tailoring the design of different car types to meet the specific comfort requirements can lead to enhanced overall comfort and satisfaction. In conclusion, this study provides valuable insights into assessing thermal comfort and indoor air quality in vehicle cabins. By addressing the unique challenges posed by the in-car environment, it offers guidance for designing and engineering vehicles that prioritize passenger comfort and well-being.

Seasonal field study on thermal comfort in university classrooms in Mediterranean climate

The assessment of indoor environmental conditions of educational buildings is not only essential to ensure the correct performance of heating, cooling and ventilation systems but it is also fundamental to guarantee a suitable environment. This study aims to analyse the thermal comfort of students in teaching–learning spaces in university buildings with natural ventilation. A field measurement campaign and a questionnaire survey were carried out from September 2021 to June 2022 in educational buildings in southern Spain. The collected data were analysed and the neutral temperature in each season was obtained, based on the thermal sensation votes of 1966. The neutral temperatures found in this study were 23.5°C, 23.1°C, 23.3°C and 23.9°C, for autumn, winter, spring and summer, respectively. The ranges obtained for 90% acceptability in the winter season (21.1°C–25.1°C) provided lower temperature limits than the ranges obtained in the summer months (22.6°C–25.3°C). The highest values for clothing insulation were found in the autumn (0.90 clo) and winter (0.75 clo) seasons, compared to the spring (0.5 clo) and summer (0.4 clo) seasons. An adaptive thermal comfort model was applied. These findings could be used to improve thermal comfort and to optimise energy consumption according to the students’ actual thermal perception.

Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Real-time clothing insulation level classification based on model transfer learning and computer vision for PMV-based heating system optimization through piecewise linearization

Achieving a balance between energy efficiency and thermal comfort is a key aspect of sustainable building design. Traditional control methods typically maintain indoor air temperature within predetermined limits, disregarding variable factors such as occupancy activity and clothing levels, which influence thermal comfort. Conversely, comfort-based control strategies present an opportunity to automate heating and cooling systems, enabling them to dynamically respond to variations in thermal comfort. To achieve this, real-time information on clothing insulation (and its adjustment) is indispensable for accurately estimating thermal comfort. In this study, we explore the potential of a novel detection approach capable of classifying clothing insulation in real-time and utilizing this information to optimize the operation of building energy systems. By doing so, the proposed method facilitates the delivery of indoor conditions tailored to user requirements and potentially reduces energy wastage. The development of a two-stage, computer vision-based framework for occupancy detection and clothing insulation classification forms the core of this approach. Leveraging deep learning algorithms, this framework performs detection and recognition tasks, even with limited training data effectively, enabling real-time classification of light, medium and heavy clothing. To address the nonlinearity of traditional predicted mean vote (PMV) models, we applied a piecewise linearization approach to our PMV-based optimal control strategy. We evaluated the performance of this detection method through initial experimental field tests conducted in a case study university building. The results demonstrate the proposed method's ability to classify clothing insulation levels and generate real-time profiles. We further analyze the impact of our proposed approach on thermal comfort and energy performance through scenario-based modelling and simulations. The initial results showed the potential of integrating our method with PMV-based controls to enhance thermal comfort and overcome the limitations of predefined values or fixed schedules. However, while our study confirms the feasibility of classifying clothing insulation levels for multiple occupants engaged in diverse indoor activities, it also highlights the need for further refinement to enhance detection accuracy and ensure seamless integration with building energy systems.

Impact of thermal control by real-time PMV using estimated occupants personal factors of metabolic rate and clothing insulation

To optimize thermal comfort for occupants’ wellbeing and health care, it's essential to adjust heating and cooling systems in real-time based on occupants' thermal preferences. For this, personal factors affect individual thermal comfort, such as metabolic rate and clothing insulation, should be estimated in real-time. The aim of this research is introducing an intelligent model capable of estimating metabolic rate and clothing insulation values from indoor images, suitable for both single and multi-occupant scenarios. Additionally, a control algorithm considering a real-time predicted mean vote (PMV), was developed using the proposed model, and its implications for thermal comfort and energy efficiency were investigated. Utilizing advanced computer vision methodologies, the model achieved a remarkable 95% training accuracy, and its reliability was further validated through experimentation. Evaluations of the PMV-based algorithm underscored its efficacy in enhancing thermal comfort relative to conventional methods in both individual and multi-occupant settings. Conversely, energy use was contingent upon the personal factors. In group settings, the mode values of metabolic rate and clothing insulation were effective for determining a representative PMV. In conclusion, the real-time PMV-based control represents a pioneering approach to augment thermal comfort using actual occupant data, paving the way for a synergistic balance between comfort augmentation and energy saving.