Thermal Comfort Of Passengers Research Articles

Investigation of passenger thermal satisfaction across multi-space transitions in high-speed railway stations: A case study in cold regions of China

Passengers in high-speed railway stations experience multiple spatial transitions; however, existing research has typically examined thermal comfort in isolation, neglecting the entire sequence of thermal experiences. This study investigates passenger thermal comfort throughout the departure process, specifically analyzing two entry modes: Mode 1 (entry via the outdoor square) and Mode 2 (entry via the underground passage). Utilizing behavioral data from 17,354 passengers surveyed in a typical high-speed railway station, we replicated these conditions in climate chamber experiments. Participants were exposed to 30 combinations of outdoor environments and four indoor temperatures (24 °C, 26 °C, 28 °C, and 30 °C). Thirty subjects, dressed in 0.49 clo insulation garments, assessed thermal satisfaction (TSAT) in each space and overall thermal satisfaction (OTSAT) for the departure process through questionnaires. Results indicated that high outdoor temperatures led to lower satisfaction in Mode 1 compared to Mode 2. Strong correlations were observed between TSAT in the waiting room and check-in area with OTSAT, underscoring their significant impact on OTSAT. Optimal thermal sequences were identified as O-26-26-26-24 for Mode 1 and U-28-26-24-24 for Mode 2, achieving OTSAT values of 1.11 and 1.12, respectively. The study also proposed two effective thermal sequences: a downward trend and a fluctuation trend, highlighting the benefits of initial discomfort followed by improvement. The recency effect emphasized the importance of effective thermal management in the waiting room and check-in area to enhance OTSAT. These findings provide valuable insights for optimizing thermal environment control in high-speed railway stations, balancing passenger comfort with energy efficiency.

Real-time prediction model of passenger thermal comfort for intelligent cabin

This article establishes a joint model of intelligent cabin and human thermal sensation, following experimental verification. And 12000 sets of datasets are obtained through joint model simulation under 120 random working conditions. Subsequently, a real-time prediction model of passenger thermal comfort is established and is trained using the Catboost algorithm based on the datasets. Finally, under various working conditions, the calculation results of the real-time prediction model were compared with these of the joint model and steady-state prediction model separately. The results indicate that the real-time prediction model can predict the passenger thermal comfort under unstable thermal environment, while the steady-state prediction model cannot. And compared to the accurate value calculated by joint model, the average mean absolute error (MAE) and mean absolute percent error (MAPE) of the real-time prediction model is 0.025 and 1.58 %, respectively. This indicates that the real-time prediction model established in this article has good accuracy.

Field experiment and simulation of hot air curtain for cold protection in subway aisle in severe cold regions

Harbin Subway is the northernmost subway line in China. In winter, outdoor temperature can reach −37.7 °C. Cold protection has emerged as a critical priority for subway operations in the region. In this paper, taking the aisle of a typical island station in Harbin as the research object, long-term and short-term field experiment on the thermal environment of the aisle were carried out. The field test data of the station and the full-scale model were used to conduct numerical simulations on the effect of supply air temperature, speed, and angle on the aisle environment. The modified PET temperature scale and average power consumption during the operation of the hot air curtain were used to evaluated the cold protection solutions of the air curtain. The results revealed that the average temperature of the cross-sections in the aisle without cold protection measures dropped by 5.4 °C–6.3 °C from its initial level after the train left the station. Without increasing average power consumption, changing the supply air angle of hot air curtain can improve the thermal environment in the aisle. Considering the three aspects of cold protection effect, passenger thermal comfort and power consumption, this paper recommends that the cold protection solution for aisles with a length less than or equal to 76.7 m, L-shaped and similar layouts is 35 °C for the supply air temperature, 9 m/s for the wind speed, and the range of the difference between the supply air angle and the aisle inclination angle is 6° <θ-α < 11°.

&lt;b&gt;Evaluation Method of Passenger Thermal Comfort Considering Effects of Airflow by Cross-flow Fan in Commuter Vehicles in Summer&lt;/b&gt;

&lt;b&gt;Evaluation Method of Passenger Thermal Comfort Considering Effects of Airflow by Cross-flow Fan in Commuter Vehicles in Summer&lt;/b&gt;

Computational Fluid Dynamics Process for Front Windshield Mist Deposition and Its Subsequent Demisting

&lt;div&gt;A vehicle’s heating, ventilation, and air-conditioning system plays a dual role in passenger thermal comfort and safety. The functional aspects of safety include the front windshield demist and deicing feature of the system. The thin-film mist is a result of condensation of water vapor on the inner side of the windshield, which occurs at low ambient temperatures or high humidity. This mist deposition depends on the air saturation pressure at the front windshield. Indian regulation AIS-084 defines the experimental setup for testing, which encompasses both the mist deposition and its subsequent demist process. This regulation mandates testing, which occurs at a later stage of product development. This performance validation can be performed using a three-dimensional computational fluid dynamics approach.&lt;/div&gt; &lt;div&gt;Current work summarizes the simulation process for both the mist deposition and the subsequent demisting phenomenon. The complexity of the flow physics is captured via the transient multiphase fluid flow phenomenon subjected to buoyancy effects. This phenomenon is simulated using the Eulerian wall film approach. The wall film deposition of the mist is modeled via species transport. Further, the near-wall thermal effects of surface conduction and heat transfer are simulated by modeling shell conduction layers. Vapor diffusion and relative humidity inside the cabin are modeled using a user-defined function. The process correlation is achieved for two categories of vehicles to establish the process efficacy and robustness. Moving ahead, a design of experiments (DOE) is planned to mitigate the need to simulate mist deposition. The DOE is planning to incorporate deviations in both the input airflow conditions and the imposed ambient conditions. The results from DOE point toward factors that might cause deviations in simulation results with respect to the test. Importantly, the study concludes that the uniform initial thickness assumption of the mist layer can be used for subsequent demist analysis.&lt;/div&gt;

Dual-phase prediction model of passenger thermal sensation using facial thermal imaging and environmental factors

The concept of passenger-centric design has become increasingly significant in the intelligent automotive industry. Enhancing in-cabin thermal comfort while maintaining energy efficiency is crucial, and to this end, we leverage real-time passenger thermal sensation as a basis for HVAC control strategies. This study introduces a non-invasive, dual-phase prediction model that evaluates passenger thermal comfort by utilizing facial thermal imaging and environmental factors. The proposed model employs a deep learning neural network to extract pertinent features from the facial thermal images for an initial prediction. Then this prediction is refined by integrating it with environmental data through a machine learning algorithm, facilitating real-time personal thermal comfort assessment. Data collection for the model was performed across two distinct seasons, with 22 participants in summer and 18 in winter, gathering facial thermal images, environmental parameters, and thermal sensation votes to construct a comprehensive dataset. Analysis revealed a set of critical environmental factors that correlate strongly with thermal sensation votes, which were used as auxiliary features in the model. The performance of the proposed dual-phase approach, combining deep learning and machine learning techniques, demonstrated robust effectiveness on both summer and winter datasets. This substantiates the method's practicality for real-world application within vehicle environments.

An intelligent thermal comfort control strategy for air conditioning of fuel cell vehicles

Thermal comfort is an important indicator for evaluating vehicle air conditioning, with many influencing factors, but traditional vehicle air conditioning control only take temperature as the control target, neglecting the passenger's actual thermal comfort situation, which cannot automatically adapt to the thermal comfort needs of different passengers. To address this issue, this paper establishes an air conditioning model based on a fuel cell vehicle (FCV) and proposes a control strategy based on thermal comfort, using deep reinforcement learning based on PPO algorithm to establish the control model. Which can automatically control the compressor and blower, this paper conducts simulation analysis under four different environmental conditions which including static and dynamic situations. The results show that when using the intelligent thermal comfort control strategy, the overall thermal sensation values of passengers are 0.006, 0.005, 0.033, and 0.102, respectively; which are 0.088, 0.333, 0.093, and 0.120 lower than the temperature-based control strategy, this proves that the control strategy proposed in this paper can achieve better thermal comfort for passengers.

Effect of air velocity and relative humidity on passengers’ thermal comfort in naturally ventilated railway coach in hot-dry indian climate

In India, railways still continue to be the most prominent means of transport, especially non-air conditioned railway that carries around 3.5 billion people every year. Thermal comfort in these non-air-conditioned trains is predominantly influenced by windows and fans. The present study orients around the effect of fan air as well as naturally ventilated air on thermal comfort of passengers. The study is part of a field survey followed by an experimental investigation that was carried out in non-air-conditioned railway coaches during summer afternoon, with more than 1500 subjects of different gender and age category. The study significantly contributes in defining an elevated acceptable range of comfort air velocity inside a moving stock. The mean threshold air velocity where passengers responded a neutral PCV (passengers' comfort vote) is found to be 2 m/s which is much higher than conventional standards for enclosed static buildings and premises. The clear dominance of behavioral adaptation has been noticed in expression of thermal comfort by the passengers. This strengthens the idea of adopting some alternative method to deal with thermal discomfort of non–air-conditioned railway passengers. Here a novel term as ‘zone of independence’ (ZoI) has been defined at which the effect of air velocity becomes independent on passengers' comfort vote. This ZoI can significantly be utilized while optimization of thermal comfort in an ICF railway coach.

Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness.

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements.

EEG-TransMTL: A transformer-based multi-task learning network for thermal comfort evaluation of railway passenger from EEG

The evaluation of thermal comfort for railway passengers holds considerable importance, not only in reducing energy consumption but also in enhancing the passengers' experience. This paper presents a Transformer-based multi-task learning network (TransMTL) designed for railway passenger thermal comfort evaluation using EEG. We utilized manual features to extract temporal and frequency information, while a Transformer encoder distilled spatial information. The multi-task learning structure enhances model robustness by leveraging thermal comfort task correlations. We conducted experiments during winter and summer with high-speed railway passengers, establishing a comprehensive EEG dataset. The results demonstrated that our proposed EEG-TransMTL model outperformed classical machine learning and deep learning models in all four thermal comfort evaluation tasks, achieving accuracy rates of 65.00%, 66.70%, 80.38%, and 71.01%, respectively. We enhanced model interpretability by visualizing attention weights from the Transformer encoder, identifying key EEG channels. A simplified model utilizing only eight crucial channels also delivered notable performance. This research provides a practical and neuro-mechanism interpretable solution for thermal comfort evaluation.

Piston effect of subway trains under different operating conditions and its influence on passenger thermal comfort

With the rapid development of rail transportation, environmental issues of subway become more and more significant, while the thermal comfort of passengers also draws wide attention. There are various arrival conditions for actual subway trains, and their effects on subway stations and passengers have rarely been studied. This paper is the first to investigate the piston effect of subway trains under single and double train arrival conditions and its impact on passenger thermal comfort. Firstly, a transient 3D numerical model based on the real metro station is established. Then, the influencing factors of the piston effect are analyzed, including the maximum travel speed of single train and the different arrival time intervals of two trains. Moreover, inhomogeneity coefficient (Kt and Ku) and the predicted mean vote (PMV) model and the predicted percentage dissatisfaction (PPD) index are introduced to analyze the thermal comfort. The results show that the two-train condition has a more uniform temperature and velocity distribution than the single-train condition. However, the best thermal comfort is obtained for the two arrival conditions of single train traveling at 16.2 m/s and two-train arrival time interval of 1s from the PMV and PPD. Besides, the fresh air volume of the two-train model is increased by 100% than that of the single-train model, which indicates that the double-train model is better than the single-train for station ventilation. These studies provide important theoretical guidance and references for optimizing metro train operation strategies and the design of thermal environments in metro stations.

Passenger thermal comfort in the whole departure process of high-speed railway stations: A case study with thermal experience and metabolic rate changes in summer

Passenger thermal comfort in the whole departure process of high-speed railway stations: A case study with thermal experience and metabolic rate changes in summer

Optimal design modeling of an energy system for a near-zero energy restaurant with green hydrogen energy storage systems

In this research, we want to analyze the consumed energy of an nZEB restaurant in a typical city to anticipate tourists. The energy of this restaurant is supplied through solar panels and electricity grids and we use the hydrogen storage tool because of its advantages to store energy. In this regard, Using the Fanger model, the thermal comfort of passengers was assessed, the energy consumption rate was obtained for the refrigerator, HVAC, lights, and systems, and the consumed water of the restaurant is supplied. Trnsys software was used for this simulation, and the transient performance of the mentioned nZEB in a year is evaluated by Trnsys software. PV panels are used to supply the consumed electricity of the building, A tank stores hydrogen and the extra produced electricity that can be used as needed if sunlight is scarce or electricity is needed. A solar collector is used to generate hot water, and a water tank is used to store the generated hot water. A heat pump is used to supply the cooling load and heating. The consumed hot water by the dwellers is supplied using the stored hot water. With the help of MATLAB software, the Fanger model is used to evaluate the thermal comfort of dwellers. The energy and performance of the restaurant refrigerator are obtained by TRNSYS and EES software. The results show that a significant part of the restaurant's electricity is supplied using 140 PV panels with 0.89 area. In addition, the initial hydrogen pressure tank was lower than the final hydrogen pressure tank. In this regard, energy for the heat pump, refrigerator, light, pump, fan, and compressors is supplied well. Using 16 square meters of solar collectors, the building gets its hot water. Furthermore, the restaurant's air conditioning system provides thermal comfort throughout the year. As a result, using the building roof area for ZEB can significantly reduce the greenhouse gas emissions of Doha.

Thermal performance evaluation of a compact two-fluid finned heat exchanger integrated with cold latent heat energy storage

To alleviate the mismatch between thermal energy supply and demand, the use of latent heat thermal energy storage of phase change materials is a reliable and effective solution in thermal systems. The present work aims to introduce an innovative finned-plate air-liquid heat exchanger with phase change material partially embedded in the fins' gap providing airside cooling by thermal energy storage during coolant shutdown periods. The thermal performance and accompanying phase change process are studied using a three-dimensional computational fluid dynamic approach. The developed model is validated with corresponding experimental data at the same geometry and operating conditions. The system performance is examined time-dependently considering the charging/discharging processes of the phase change material. The effect of air mass flow rate variation is studied, and the results are discussed in terms of fluids temperature, heat transfer rates, and the phase change material phase transition process. The results demonstrate that the heat exchanger stores excess thermal energy during the charging process, which is later discharged for air cooling during demand periods. It is observed that the use of phase change material provides a cooling load of 188 kJ and over 9 min of extra cooling time for the airside in the discharging process. The share of latent heat transfer in the discharging process is calculated at an average of 54% of the total heat transfer. Obtained results reveal that increasing the air mass flow rate can reduce the rate of discharged cold thermal energy and cooling time. The presented thermal management system offers a unique solution to manage passenger thermal comfort in hybrid and electric vehicles with a start-stop function. The introduced system could provide efficient cabin air cooling and enable effective energy savings during short periods of engine shutdown.

Modelling the Electric Bus Charging Network Requirements for HVAC Purposes

Wider use of electric buses is hindered by necessity to provide cabin heating for the passengers during winter months, which in cold climates uses up as much energy as driving, thus significantly reducing bus driving range and requiring larger traction batteries, which are expensive. When the lifetime operational costs are included, unless they are heavily subsidized, the battery electric buses have troubles competing with internal combustion engine (ICE) buses in cold weather conditions. For typical ICE buses, the power capacity for heaters is in the range of 50 kW. It would be possible to increase the driving range by using heat accumulators to provide energy for vehicle thermal requirements. These heat accumulators could be recharged in bus stops using wireless charging to minimize battery size. This article describes the results of a mathematical model developed to determine optimum wireless charging power requirements for heat accumulators. The model is created for public transportation system in Riga city based on weather data in years 2017–2022 to provide passenger thermal comfort. The model simulates energy flows for bus movement and heating purposes using worst-case scenario approach. The model analysed 198 916 bus stop data, which were pertinent for weekday travels in Riga city. The analysis covered 1716 individual bus stops and 448 routes. The results showed that the charging availability during the day varied from 3 % to 55 % and on average the total energy needed for heating would be 75.6 kWh. However, in the worst-case scenario, the number rises to 237 kWh making the system too expensive for practical applications. During the research there is a very limited availability of research regarding bus heating requirements. Most of the research on thermal comfort in busses are done in hot climate and consequently mostly concerns air conditioning. The thermal comfort research about cold weather is predominantly about buildings and indoor comfort and could not directly apply, as clothing should be considered. Also, the research shows that the temperatures for comfort are lower for short haul vehicles than for the long-haul ones. All these factors strongly indicate that current conventions on temperature requirements for buses are outdated and coming from ICE buses where the heat came from the engine and was free. Therefore, the next studies should include field research analysing passenger thermal comfort levels in buses in winter to explore if there are additional opportunities for energy savings in bus HVAC systems.

Improving Electric Vehicle Range and Thermal Comfort through an Innovative Seat Heating System

In the last decade, car manufactures invested a lot of effort to align their products to the latest energy directives which encourage the production and usage of electrified vehicles to reduce the greenhouse gases production. This resulted in several important developments, which enhanced the advantages of electric vehicles in terms of local emissions (zero tailpipe emissions), efficiency, convenience in urban areas and others and ultimately led to their ever-increasing adoption. However, there are still some challenges that need to be addressed. One example is the negative influence of low (winter) and high (summer) atmospheric temperatures on electric vehicle range due to the cabin temperature heating and cooling. This requires more efficient ways of using energy to avoid sacrificing the passenger thermal comfort for an increased vehicle range. The present study proposes a new strategy for heating the seats in electrically powered vehicles using an uneven distribution of the heating elements. The uneven positioning of the heating elements is based on the thermal sensitivity of the human skin measured data and scientific literature. For this, a thermal sensitivity test device was developed to map the human skin thermal sensitivity. To test the new solution, a vehicle seat was equipped with heating pads (arranged according to the position of the relevant human skin thermal sensitivity points). For the next step, comparative measurements (power consumption, temperature distribution—with an IR camera—and human subjectivity test) were carried out between a classical vehicle seat heating system and the newly proposed heating solution. The outcome of the study revealed that the proposed heating system will supply at least the same thermal comfort sensation as the standard vehicle seat but using only half of the energy consumption, which translates in an increase of the electrically powered vehicle range between 1.2% and 1.5%, depending on the climate and driving conditions (over the WLTC). For example, a vehicle with a 16 kWh battery driving over the WLTC in Frankfurt climate conditions can gain in 1 year between 139.6 and 164.5 km.

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.

Analysis of Human Thermal Comfort in Bus Based on Thermoelectric Cooling Climate-Controlled Seats

In order to improve the thermal comfort of passengers, the thermoelectric cooling climate-controlled seats were set in the passenger cabin. In this study, thermoelectric cooler units’ cooling performance is studied by experiment method. The heat and flow field of the bus cabin are analyzed in four different cases (air conditioner open/closed, thermoelectric cooler open/closed) using computational fluid dynamics method. Moreover, an experiment has been conducted to verify the accuracy of the simulation method. The results show as below: the ideal working current of the thermoelectric cooler is in range of 3-4 A and the coefficient of performance value range 0.5-0.7. The climate-controlled seats can enhance the local airflow perturbation, which can increase the passengers’ overall thermal comfort by 18.96%. The thermal comfort of passengers closer to air conditioner inlet is better than passengers in other areas, and the area with the best thermal comfort for passengers are the front and rear seats of the bus cabin. As expected, the thermoelectric cooling climate-controlled seat can improve the thermal comfort of passengers, and has a broad application prospect.

Contribution of the thermal inertia of trains to the primary frequency control of electric power systems

There is a wide consensus about substituting fossil fuel-based electric generation with renewable resource-based one. However, the displacement of conventional power plants with power electronics-interfaced plants implies several technical issues that must be addressed. Among these needs, providing ancillary services to electric power systems is fundamental to maintain power quality and system reliability. There is a considerable amount of literature about providing ancillary services from renewable power plants and an increasingly important effort to complement these services with loads. However, the contribution of railway systems to these services has been less explored. Considering the world-wide expansion of railway systems, it is appropriate to consider them as relevant contributors to ancillary services. The present work presents a novel approach to assess the possibilities of contribution of railways to grid primary frequency control based on the thermal inertia of trains. This approach aims at determining how railways can support frequency control tasks without affecting the circulation time of trains and thermal comfort of passengers. This paper proposes to act on the power consumption of the air conditioning system of trains as the way of making the train whole consumption responsive to frequency deviations. In addition, the paper proposes a methodology for analyzing the effectivity of the approach. It defines the extent of the modeling detail of railway and electric power system, and the coupling between them. To validate the feasibility, its application is illustrated with a case study. The results show that frequency deviations can be reduced without affecting passengers’ thermal comfort.

Numerical thermal performance analysis of a PCM-to-air and liquid heat exchanger implementing latent heat thermal energy storage

Due to the mismatches in energy supply and demand in thermal systems, employing latent heat thermal energy storage using phase change materials (PCMs) is a reliable and effective solution. In this regard, this paper introduces an innovative PCM-to-air and liquid heat exchanger to increase thermal system performance by providing a hybrid heat source to the airside. A novel numerical work based on multiphysics coupling of heat transfer and double fluid flow and phase change within a complex geometry is represented. Numerical heat transfer analysis is performed on the model based on three-dimensional computational fluid dynamics simulation. In order to make a thorough thermal performance assessment, the dynamic behaviour of the system is investigated for both PCM charging and discharging processes. Furthermore, the effect of airside flow variation on the thermal response of the system is studied, and the results are discussed based on fluids temperature, heat transfer rate, and the PCM phase transition procedure. It is demonstrated that the PCM can store excess heat from the working fluid during the charging process and releases it to the airside during the discharging process. The heating load of 323 kJ is stored during the charging process which has enabled the PCM to provide up to 6 min of extra airside heating time during discharging. The share of PCM latent component of the airside heat transfer is determined to be around 48 %. It is also observed that by increasing the airflow rate, the discharged heat load is decreased slightly, and the heating time is reduced notably. The presented thermal energy storage system offers a unique solution for the start-stop function implemented in many hybrid and electric vehicles. During short periods of engine shutdown, the system could provide passenger thermal comfort and enable effective energy savings.