Thermal Comfort Of Users Research Articles

Performance Analysis of Heat Transfer and User Thermal Comfort on Latent Heat Treatment of Scald Wound Based on Spray Cooling

Performance Analysis of Heat Transfer and User Thermal Comfort on Latent Heat Treatment of Scald Wound Based on Spray Cooling

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

Research on the adjustable range of air conditioner setting temperature considering user comfort

Abstract Traditional air conditioner (AC) temperature control typically sets temperature goals or establishes a uniform temperature range without considering industrial characteristics and user comfort preferences. As a result, users cannot achieve differentiated and precise control. This paper proposes a data-driven approach to predict the range of AC set temperatures under the constraint of user comfort. Firstly, a random forest feature importance screening method is employed to extract key factors influencing AC set temperatures. Secondly, a convolutional neural network (CNN) model is constructed to estimate AC set temperatures for five typical user categories. Finally, based on the Predicted Mean Vote (PMV) model, user thermal comfort constraints are established to calculate the flexible adjustment range of AC for users. Through simulation analysis using real AC data from a certain region in Eastern China, the proposed standard for estimating user air conditioner set temperatures in this paper ensures a root mean square error not exceeding 0.5 and an average absolute percentage error not surpassing 1.8%. The obtained air conditioner adjustment ranges reflect the characteristics of users in different industries, meeting engineering requirements.

Enhancing operational optimization of district heating substations through refined estimations of network campus buildings heat demands to achieve a low return from the network

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, ▪ penalties are inputs used by the optimizer to find the optimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Analyzing the Thermal Characteristics of Three Lining Materials for Plantar Orthotics.

The choice of materials for covering plantar orthoses or wearable insoles is often based on their hardness, breathability, and moisture absorption capacity, although more due to professional preference than clear scientific criteria. An analysis of the thermal response to the use of these materials would provide information about their behavior; hence, the objective of this study was to assess the temperature of three lining materials with different characteristics. The temperature of three materials for covering plantar orthoses was analyzed in a sample of 36 subjects (15 men and 21 women, aged 24.6 ± 8.2 years, mass 67.1 ± 13.6 kg, and height 1.7 ± 0.09 m). Temperature was measured before and after 3 h of use in clinical activities, using a polyethylene foam copolymer (PE), ethylene vinyl acetate (EVA), and PE-EVA copolymer foam insole with the use of a FLIR E60BX thermal camera. In the PE copolymer (material 1), temperature increases between 1.07 and 1.85 °C were found after activity, with these differences being statistically significant in all regions of interest (p < 0.001), except for the first toe (0.36 °C, p = 0.170). In the EVA foam (material 2) and the expansive foam of the PE-EVA copolymer (material 3), the temperatures were also significantly higher in all analyzed areas (p < 0.001), ranging between 1.49 and 2.73 °C for EVA and 0.58 and 2.16 °C for PE-EVA. The PE copolymer experienced lower overall overheating, and the area of the fifth metatarsal head underwent the greatest temperature increase, regardless of the material analyzed. PE foam lining materials, with lower density or an open-cell structure, would be preferred for controlling temperature rise in the lining/footbed interface and providing better thermal comfort for users. The area of the first toe was found to be the least overheated, while the fifth metatarsal head increased the most in temperature. This should be considered in the design of new wearables to avoid excessive temperatures due to the lining materials.

Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks

To reduce global carbon emissions and limit climate change, controlling energy consumption in buildings is an important piece of the puzzle. Here, we specifically focus on using a demand response (DR) algorithm to limit the energy consumption of a residential building's heating system while respecting user's thermal comfort. In that domain, Reinforcement learning (RL) methods have been shown to be quite effective. One such RL method is Monte Carlo Tree Search (MCTS), which has achieved impressive success in playing board games (go, chess). A particular advantage of MCTS is that its decision tree structure naturally allows to integrate exogenous constraints (e.g., by trimming branches that violate them), while conventional RL solutions need more elaborate techniques (e.g., indirectly by adding penalties in the cost/reward function, or through a backup controller that corrects constraint-violating actions). The main aim of this paper is to study the adoption of MCTS for building control, since this (to the best of our knowledge) has remained largely unexplored. A specific property of MCTS is that it needs a simulator component that can predict subsequent system states, based on actions taken. A straightforward data-driven solution is to use black-box neural networks (NNs). We will however extend a Physics-informed Neural Network (PiNN) model to deliver multi-timestep predictions, and show the benefit it offers in terms of lower prediction errors (−32% MAE) as well as better MCTS performance (−4% energy cost, +7% thermal comfort) compared to a black-box NN. A second contribution will be to extend a vanilla MCTS version to adopt the ideas applied in AlphaZero (i.e., using learned prior and value functions and an action selection heuristic) to obtain lower computational costs while maintaining control performance.

An Optimization Control Method of IEH Considering User Thermal Comfort

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system’s operating costs and improve energy efficiency while satisfying user thermal comfort demand.

Tensile Membranes and Public Spaces Thermal Comfort: An Analytical Review

Urbanization altered urban microclimates intensifying the Urban Heat Island (UHI) effect and thereby increasing outdoor thermal discomfort. Tensile membrane structures (TMS), due to their shading and cooling properties, present a viable solution for enhancing thermal comfort in urban public spaces. This paper synthesizes numerical outcomes from several studies integrating empirical data, computational models, and subjective assessments to evaluate the impact of TMS on thermal comfort. Results indicate that TMS can reduce ambient temperatures by 2-3°C and Physiological Equivalent Temperature (PET) by approximately 2.3-2.4°C or up to 5.4ºC on Mean Radiant Temperature (MRT) values, therefore enhancing user thermal comfort. The study underscores the critical role of design factors such as form, aperture, location, and strategic placement in optimizing TMS effectiveness. It also highlights the importance of considering local climatic conditions and user behaviours in the design and implementation of shading solutions. Subjective assessments reveal higher comfort levels in shaded areas, emphasizing the psychological benefits of TMS. Overall, TMS not only mitigate heat stress but also enhances the aesthetic and functional quality of urban environments, contributing to sustainable and liveable urban development. This comprehensive review provides valuable insights for urban planners, designers, and policymakers in creating more comfortable and resilient public spaces.

Evaluation of thermal properties and thermoregulatory impacts of lower back exosuit using thermal manikin

As exoskeletons and exosuits rapidly transition from laboratory to practice, the thermal discomfort posed by wearing the devices is emerging as a significant challenge towards their widespread adoption. We use a thermal manikin coupled with a thermoregulation model to evaluate the thermal properties and thermophysiological impacts of such devices. We measure thermal and evaporative resistances of summer and full clothing coupled with a legacy low-back supporting exosuit and its successor that features two design alterations to improve the user's thermal comfort. We quantify the decrease in evaporative resistance provided by substantial perforation of the back portion of the updated exosuit. The thermal manikin can replicate local skin temperature decrease associated with a release of a dual-mode thigh body attachment measured during a prior human trial. Using the thermal manikin coupled with a thermoregulation model, we simulate how the updated exosuit with several levels of assumed metabolic rate reduction impacts sweat rate, skin and core temperature changes during multiple work-rest cycles in a hot and humid climate. While a large metabolic rate reduction (>15%) is required to significantly slow the core temperature increase, even a minor metabolic rate reduction (5%) could provide a substantial reduction (20%) in the sweat rate (i.e. could reduce dehydration). Results suggest that thermal manikins with a thermoregulation model are an effective and efficient platform for comparing exosuit design features and for improving their thermal aspects. Our study highlights the comprehensive method and importance of considering thermal aspects when designing exosuits for occupational use.

Development of a parametric optimization concept for the sun control of the outdoor spaces: the case study of 5A business park, Cairo, Egypt

PurposeComfortable outdoor workspaces are important for employees in business parks and urban areas. Prioritizing a pleasant thermal environment is essential for employee productivity, as well as the improvement of outdoor spaces between office buildings to enhance social activities and quality of outdoor workplaces in a hot arid climate has been subjected to very little studies Thus, this study focuses on business parks (BPs) landscape elements. The objective of this study is to enhance the user's thermal comfort in the work environment, especially in the outdoors attached to the administrative and office buildings such as the BPs.Design/methodology/approachThis research follows Four-phases methodology. Phase 1 is the investigation of the literature review including the Concept and consideration of BP urban planning, Achieving outdoor thermal comfort (OTC) and shading elements analysis. Phase 2 is the case study initial analysis targeting for prioritizing zones for shading involves three main methods: social assessment, geometrical assessment and environmental assessment. Phase 3 entails selecting shading elements that are suitable for the zones requiring shading parametrize the selected shading elements. Phase 4 focuses on the optimization of OTC through shading arrangements for the prioritized zones.FindingsShading design is a multidimensional process that requires consideration of various factors, including social aspects, environmental impact and structural integrity. Shading elements in urban areas play a crucial role in mitigating heat stress by effectively shielding surfaces from solar radiation. The integration of parametric design and computational optimization techniques enhances the shading design process by generating a wide range of alternative solutions.Research limitations/implicationsWhile conducting this research, it is important to acknowledge certain limitations that may affect the generalizability and scope of the findings. One significant limitation lies in the use of the shade audit method as a tool to prioritize zones for shading. Although the shade audit approach offers practical benefits for designers compared to using questionnaires, it may have its own inherent biases or may not capture the full complexity of human preferences and needs.Originality/valueFew studies have focused on optimizing the type and location of devices that shade outdoor spaces. As a result, there is no consensus on the workflow that should regulate the design of outdoor shading installations in terms of microclimate and human thermal comfort, therefore testing parametric shading scenarios for open spaces between office buildings to increase the benefit of the outer environment is very important. The study synthesizes OTC strategies by filling the research gap through the implementation of a proper workflow that utilizes parametric thermal comfort.

An Optimal Operation Strategy of Regenerative Electric Heating Considering the Difference in User Thermal Comfort

Regenerative electric heating has gradually become one of the main forms of winter heating with the promotion of “coal to electricity” project. By fully exploiting its regulating capacity, it can effectively achieve a win–win situation of “peak shaving and valley filling” on the grid side and “demand response” on the customer side. In order to meet the different heating demands of users, a regenerative electric heating optimization and control strategy is proposed, taking into account the difference in users’ thermal comfort. Firstly, the reasons for the difference in user thermal comfort are analyzed, and the differentiated preference factors are calculated based on the maximum likelihood estimation method to design differentiated heating schemes. Then, a dynamic optimization and control model for regenerative electric heating with comfort and economic evaluation indicators is established and solved by using quantum genetic algorithm. Finally, a numerical example is used for simulation analysis. The research results show that the strategy proposed in this paper can take into account the comfort of customers and the economy of peaking and low load shifting, so that the operation of regenerative electric heating can respond to the different needs of different customer groups, and realize flexible adjustment at any time of the day.

A climate sensitive design approach to BIPV: Investigating the nexus between solar energy and thermal comfort in cities in Sweden and Cyprus

The research performed attempts to answer the question of how building integration of active solar systems may affect the thermal comfort in open areas and the interstitial space between buildings in urban environments. This is done by using computer simulation and in-situ observations at the extreme northern and southern geographies of Europe, namely in Luleå, Sweden and in Limassol, Cyprus. A typical example of the urban grid of each city is chosen and active solar systems are integrated on the facades of buildings, respectively foreach case. The thermal conditions at street level are then simulated, using Envi-MET, before and after systems integration, with the aim of assessing the differences between low and high insolation conditions, using the Physiological Equivalent Temperature (PET) indicator. Subsequently, the thermal conditions in the public space between buildings were once again assessed, with reduced emissivity values for the building integrated PV panels. The results point to the fact that the building integration of PVs lacking low emissivity coatings can have an impact in the thermal comfort of users in the locations specified, especially in the summer, wherein it is shown to be negligible in the southern case study but more significant in the northern one.

STUDY RECOMMENDATIONS TO ACHIEVE THERMAL COMFORT IN AN EDUCATIONAL BUILDING

This paper evaluates natural ventilation effectiveness in one fully dependent Air Conditioned-Educational Building in Depok to recommend potential passive cooling approaches toward user thermal comfort. This study involves building surveys to measure the temperature and humidity of three selected classrooms, A, B, and C, with varying configurations and capacities. Airflow simulation using Computational Fluid Dynamics (CFD) is done under two conditions: open and closed doors. A set of parameters, which are room configuration, type of window, and ventilation strategy, are set to evaluate natural ventilation aspects. The site survey indicates that only one classroom with an area of 92 sqm facing to the southside is classified as efficiently warm (Room A). The simulation demonstrates that cross-ventilation only occurs when the door is opened. The parameters indicating shape, dimension, type, and area of natural ventilation matter, show that the building’s natural ventilation is ineffective in providing thermal comfort. This study recommends that the building’s natural ventilation be placed according to the direction of the airflow, adding vertical fin elements, and increasing the openings by more than 5% area from the floor area.

Demand–Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization

Electric Storage Water Heaters (ESWH) are a widespread solution to supply domestic hot water (DHW) to dwellings and other applications. The working principle of these units makes them a great resource for peak shaving, which is particularly important due to the level of penetration renewable energies are achieving and their intermittent nature. Renewable energy deployment in the electricity market translates into large electricity price fluctuations throughout the day for individual users. The purpose of this study was to find a demand–response strategy for the activation of the heating element based on a multiobjective minimization of electricity cost and user discomfort, assuming a known DHW consumption profile. An experimentally validated numerical model was used to perform an evaluation of the potential savings with the demand–response optimized strategy compared to a thermostat-based approach. Results showed that cost savings of approximately 12% can be achieved on a yearly basis, while even improving user thermal comfort. Moreover, increasing the ESWH volume would allow (i) more aggressive demand–response strategies in terms of cost savings, and (ii) higher level of uncertainty in the DHW consumption profile, without detriment to discomfort.

An Advanced Fractional Order Method for Temperature Control

Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios.

Outdoor Human Thermal Comfort along Bike Paths in Balneário Camboriú/SC, Brazil

This research is concerned with understanding the degree of human thermal (dis)comfort in connection with the various microclimates present in the vicinity of bike trails in Balneário Camboriú/SC, Brazil, throughout the summer. Local Climate Zones were determined using the Sky View Factor and were identified along research routes and schedules at 9:00 a.m. and 4:00 p.m. on a subtropical summer day (14 January 2022). Data were collected with weather devices attached to the bicycle, measuring air temperature, relative humidity, and globe temperature, from which the mean radiant temperature was calculated. The PET and UTCI indices were used to assess outdoor thermal comfort in the summer. The findings revealed that at 9:00 a.m., the eastern half of the city had a higher tendency for thermal discomfort; however, at 4:00 p.m., this same location had thermal comfort for users along bike routes. At 4:00 p.m., the PET index indicated that 24% of the bike paths were pleasant, and the UTCI index indicated that 100% of them were in thermal comfort. At 9:00 a.m., the majority of the city was under discomfort conditions. The index values reflect the morning time, and the study shows that there is now a negative correlation between the SVF and the indexes, which means that the greater the SVF computations, the lower the values are. The PET and UTCI indices revealed a positive association in the afternoon period: The greater the SVF values, the higher the PET and UTCI indices. Further research should be conducted in the future because many parameters, such as construction, position, and urban (im)permeability, as well as sea breeze and solar radiation, can have a significant impact on outdoor human thermal comfort in Balneário Camboriú, and when combined with the type of LCZ and the SVF, it is possible to understand how all of these active systems interact and form microclimates that are beneficial to bike path users.

Day‐ahead optimal scheduling of building energy microgrids based on time‐varying virtual energy storage

The thermal inertia of a building envelope endows a building with a heat storage capability, introducing scheduling flexibility to a building energy microgrid (BEM). The flexibility is usually modelled as virtual energy storage (VES) and used to optimize the operation of BEMs to reduce electricity costs. However, the VES capacity is impacted by and varies with variations in indoor/outdoor temperature. If only the building envelope's effect on heat transfer is considered, without proper quantification of scheduling flexibility provided by the building envelope, the scheduling scheme (especially VES charging/discharging schemes) will deviate from the actual VES operating conditions, which may affect the thermal comfort of individuals in buildings or bring high electricity costs. In this paper, a time-varying building VES model (TVES) with three time-varying parameters (virtual electric capacity (VEC), state of charge (SOC), and charge and discharge power) is proposed to quantify the electricity storage capability of the VES at different time periods for participating in operation of the BEM. Based on the TVES, a day-ahead optimal scheduling strategy is developed for the BEM to simultaneously reduce the electricity cost and guarantee the user's thermal comfort, where the proposed time-varying parameters of TVES are taken as constraints in the optimization. Numerical studies verify the effectiveness of the proposed TVES and optimal scheduling strategy.

The Influence of Trees and Water Features on Human Health and Thermal Comfort in Hot Arid Climate at The Microclimate Level

The primary objective of the study is to determine if plants and water can lower particulate matter (PM) and carbon dioxide (CO2) levels in microclimates while simultaneously enhancing user thermal comfort. There are several implications of urban air pollution on human health, ranging from eye discomfort to fatality. The previous studies investigated the human thermal comfort in term of temperature and humidity. The purpose of this study is to evaluate thermal comfort and air pollution in two microclimates of a Tucson, Arizona college building. The trees and water features have increased the relative humidity by 110 percent over baseline levels. Consequently, the temperature fell by 19%. This significant microclimate improvement will put the majority of outdoor areas inside the thermal comfort zone for humans. The trees and water had a considerable influence on PM levels, decreasing PM2.5 by 50 percent and PM10 by 55 percent. In this research, however, the C3 type Calvin cycle caused a 4.8% increase in CO2. The trees may lower CO2 in other senior with a higher CO2 content, hence decreasing the possibility that the C3 cycle will be initiated. The ability to minimize air pollutants while simultaneously enhancing temperature conditions would result in a microclimate that is conducive to a variety of activities.

Real-Time Construction of Thermal Model Based on Multimodal Scene Data

In commercial buildings, the total consumption of central air conditioning accounts for about 40%–50%. However, at present, the initial design value of building Heating Ventilation and Air Conditioning (HVAC) is usually far greater than the actual refrigeration value of refrigeration demand, which will lead to great energy consumption waste. Moreover, the operation of HVAC affects the thermal comfort of users, so it is necessary to establish a thermal model for the scene to control. The thermal model describes the temperature of the scene in different environments. So it is very important to design a thermal model to calculate the scene in real time. Because the flow of people, the opening of windows, the ventilation of the scene and other parameters influence the change of thermal state in the scene environment, these parameters are complicated to model. Human disturbance will lead to the instability of the state of the scene environment. The inconsistency of its thermal model will lead to energy allocation tracking strategies in different regions. To solve this problem, We propose a thermal model for building thermal comfort using a multimodal analysis framework. This paper analyzes multiple temperature and humidity sensors and area image by multimodal combination and processes the image and sensor data by combining CNN and LSTM. Our results show that when the thermal model analyzed by this method is deployed in a building in the south of China, the MSE accuracy of the local effect of temperature field prediction reaches 99%, and its AMAX reaches 94%, so the running stability of the model in the scene is high. In addition, the research shows that the thermal model analysis framework can make the Internet of Things (IoT) in buildings more intelligent, and it can be combined with this thermal model to improve human comfort, make it easier to deploy in each hot zone, and have a better overall energy-saving effect.

Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method

Due to the high integration of renewable energy, renewable energy curtailment has become a key issue that needs to be solved urgently. Aiming at the areas with the characteristics of curtailed wind energy and strong thermal demand, a multi-time scale optimal scheduling model for improving the wind power integration in heating seasons was proposed. In the framework, the enhanced-interval optimization method (EIOM) was applied, by considering the uncertainties of wind power and loads in an integrated electricity and district heating system. Considering the dynamic characteristics of the heating network and the thermal inertia of building, the factors affecting users comfort are analyzed. Then the comfort model parameters based on different user types are designed. The EIOM was reformulated by decomposing into two sub-models. A test system including a 30-node electric network and two 15-node heating networks was used to apply the proposed model. The simulation proves that the EIOM can solve the impact of load uncertainty and obtain the optimal operation cost.