3A26 Modeling of thermal environment and evaluation of thermal comfort in railway vehicle cabin by CFD and human thermal model(Customer Environment)

The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Throughout the years, research has described the connection between thermal comfort and productivity. Mathematical models have been established in the attempt to predict changes in productivity according to thermal variations in the environment. Some of these models have failed for a number of reasons, including the understanding of the effect that several environment variables have had on performance. From this context, a systematic literature review was carried out with the aim of verifying the connection between thermal comfort and productivity and the combinations of different thermal and personal factors that can have an effect on productivity. A hundred and twenty-eight articles were found which show a connection between productivity and some thermal comfort variables. By means of specific inclusion and exclusion criteria, 60 articles were selected for a final analysis. The main conclusions found in this study were: (i) the vast majority of research uses subjective measures and/or a combination of methods to evaluate productivity; (ii) performance/productivity can be attained within an ampler temperature range; (iii) few studies present ways of calculating productivity.

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Research on vehicle cabin temperature and thermal comfort optimal control based on fuzzy PID

Journal of Physics Conference Series ◽

10.1088/1742-6596/1865/3/032039 ◽

2021 ◽

Vol 1865 (3) ◽

pp. 032039

Author(s):

Changhao Piao ◽

Weiwei Wang ◽

Ziyang Liu ◽

Cunxue Wu ◽

Rongdi Yuan

Keyword(s):

Optimal Control ◽

Thermal Comfort ◽

Fuzzy Pid ◽

Vehicle Cabin ◽

Cabin Temperature

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Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽

10.3390/en14030696 ◽

2021 ◽

Vol 14 (3) ◽

pp. 696

Author(s):

Eun Ji Choi ◽

Jin Woo Moon ◽

Ji-hoon Han ◽

Yongseok Yoo

Keyword(s):

Neural Network ◽

Thermal Comfort ◽

Deep Neural Network ◽

Mean Squared Error ◽

Thermal Environment ◽

The Body ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Body Parts ◽

Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

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How Can We Adapt Thermal Comfort for Disabled Patients? A Case Study of French Healthcare Buildings in Summer

Energies ◽

10.3390/en14154530 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4530

Author(s):

Youcef Bouzidi ◽

Zoubayre El Akili ◽

Antoine Gademer ◽

Nacef Tazi ◽

Adil Chahboun

Keyword(s):

Thermal Comfort ◽

Thermal Environment ◽

Residential Buildings ◽

Linear Regression Analysis ◽

Environmental Parameters ◽

Physical Parameters ◽

Mean Radiant Temperature ◽

Operative Temperature ◽

Neutral Temperature ◽

Radiant Temperature

This paper investigates adaptive thermal comfort during summer in medical residences that are located in the French city of Troyes and managed by the Association of Parents of Disabled Children (APEI). Thermal comfort in these buildings is evaluated using subjective measurements and objective physical parameters. The thermal sensations of respondents were determined by questionnaires, while thermal comfort was estimated using the predicted mean vote (PMV) model. Indoor environmental parameters (relative humidity, mean radiant temperature, air temperature, and air velocity) were measured using a thermal environment sensor during the summer period in July and August 2018. A good correlation was found between operative temperature, mean radiant temperature, and PMV. The neutral temperature was determined by linear regression analysis of the operative temperature and Fanger’s PMV model. The obtained neutral temperature is 23.7 °C. Based on the datasets and questionnaires, the adaptive coefficient α representing patients’ capacity to adapt to heat was found to be 1.261. A strong correlation was also observed between the sequential thermal index n(t) and the adaptive temperature. Finally, a new empirical model of adaptive temperature was developed using the data collected from a longitudinal survey in four residential buildings of APEI in summer, and the obtained adaptive temperature is 25.0 °C with upper and lower limits of 24.7 °C and 25.4 °C.

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