Experimental and numerical studies on indoor thermal comfort in fluid flow: A case study on primary school classrooms

Xia, Yaowen; Lin, Wenxian; Gao, Wenfeng; Liu, Tao; Li, Qiong; Li, Anran

doi:10.1016/j.csite.2020.100619

Cited by 29 publications

(11 citation statements)

References 15 publications

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“…It also has a positive effect on people's mental state and psychological feelings, and a good light environment can effectively guarantee the eyesight and teaching and learning efficiency of teachers and students. 5,[29][30][31][32] Although the acoustic and air quality environments do not affect building energy consumption, they are still important for controlling the level of noise and CO 2 concentrations in the classroom environment. Noise can make students anxious and adversely affect their learning efficiency and may cause resonance and even harm to the building structure.…”

Section: Calculation Of Evaluation Index Weightmentioning

confidence: 99%

Evaluating the overall comfort of undergraduates by using the punishment substitution method

Liu

Shi

2022

Indoor and Built Environment

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In this paper, the overall comfort of humans in thermal, light, acoustic and air quality environments was studied in a university classroom in Shanghai, and the actual overall comfort vote index was evaluated. The dimensionless function of each environmental evaluation index and human comfort level was established by a combination of subjective questionnaire assessment and objective parameter measurement, and the weight of each environmental evaluation index was obtained by the method of vector similarity. The overall comfort evaluation model was synthesized by using the punishment substitution method, dimensionless function and weight. The influence of different environmental combinations on the overall comfort evaluation model was explored, and the results indicated that the more a single evaluation index deviated from its comfort zone, the more obvious the change it caused in comprehensive comfort. The model can be used to adjust classroom environmental parameters to improve comprehensive comfort, and the results should provide a reference for evaluating the overall comfort of the classroom environment and a reasonable basis for classroom environment design parameters.

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Section: Calculation Of Evaluation Index Weightmentioning

confidence: 99%

Evaluating the overall comfort of undergraduates by using the punishment substitution method

Liu

Shi

2022

Indoor and Built Environment

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“…Many scholars have studied the internal thermal environments of buildings in different climatic regions. Taking a primary school classroom as an example, some scholars used the ANSYS FLUENT software to perform a series of three-dimensional numerical simulation experiments and numerical studies on indoor fluid flow thermal comfort [5] . Considering the green walls around the buildings as the research object, the best vertical greening mode for indoor thermal comfort in the cold winter and hot summer areas is determined [6] .…”

Section: Introductionmentioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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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 (R 2 / as the evaluation indicator (R 2 : 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 (R 2 : 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.

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“…Until now, there are very few CFD studies in schools where thermal comfort is analyzed considering a large number of students; in [ 26 , 27 ], classrooms without air conditioning and with open windows were studied. On the other hand, other CFD studies inside spaces have considered the transport of particles to study the transmission of contagious diseases in schools [ 28 , 29 ] and hospitals [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

Ovando-Chacon

León

Ovando-Chacón

et al. 2022

IJERPH

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Due to the current COVID-19 pandemic, guaranteeing thermal comfort and low CO2 levels in classrooms through efficient ventilation has become vitally important. This study presents three-dimensional simulations based on computational fluid dynamics of airflow inside an air-conditioned classroom located in Veracruz, Mexico. The analysis included various positions of an air extractor, Reynolds numbers up to 3.5 × 104, four different concentrations of pollutant sources, and three different times of the day. The simulations produced velocity, air temperature, and CO2 concentrations fields, and we calculated average air temperatures, average CO2 concentrations, and overall ventilation effectiveness. Our results revealed an optimal extractor position and Reynolds number conducive to thermal comfort and low CO2 levels due to an adequate ventilation configuration. At high pollutant concentrations, it is necessary to reduce the number of students in the classroom to achieve safe CO2 levels.

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Experimental and numerical studies on indoor thermal comfort in fluid flow: A case study on primary school classrooms

Cited by 29 publications

References 15 publications

Evaluating the overall comfort of undergraduates by using the punishment substitution method

Evaluating the overall comfort of undergraduates by using the punishment substitution method

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Computational Study of Thermal Comfort and Reduction of CO2 Levels inside a Classroom

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