Testing of Method for Assessing of Room Thermal Stability

Charvátová, Hana; Zálešák, Martin

doi:10.1051/matecconf/201712502035

Cited by 2 publications

(6 citation statements)

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“…This paper presents a study of the thermal stability of the model with various geometry sizes and material properties of all elements of the room. Compared to the previously published models of a heated (cooled) room [32,34], the thermal insulation of the outer wall is now incorporated into the model, which allows its much wider use by testing of thermal distribution inside the studied room and searching for the optimal thermal insulation material properties. Additionally, the graphical user interface created with Application Builder provides engineers and scientists the opportunity to use the model without necessarily having knowledge of COMSOL Multiphysics control.…”

Section: Discussionmentioning

confidence: 99%

“…It is assumed that the future work will include studies related to visualization in engineering [39], modelling of temperature distribution both inside the room and in the walls [30,32,34], and the study of comfortable indoor environment to keep emotional and physical states of individuals healthy [40]. Simulations or thermal imaging [39] for breathing analysis using thermal and depth imaging camera video records will be included in the future research too.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we follow the paper [34], which presents the interim results of the research on the developing methodological procedure which could be used for assessment of the thermal stability of buildings with regards to their thermal accumulative parameters concerning compliance with valid European technical standards [35] used in building industry and architecture. We describe computer application programmed in the Application Builder of the COMSOL Multiphysics software, which can simulate cooling of a room as a 3D model under the selected winter condition.…”

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

2018

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This paper is devoted to modelling of temperature distribution and its time evolution in rooms with specific thermal insulation and heat transfer for different external conditions. The simulation results should help to design the room architecture and wall materials to reduce energy losses due to heating or cooling, and to increase the inside thermal comfort. For this purpose, a methodological procedure using real data processing in the COMSOL Multiphysics modelling environment and spatial visualization of temperature evolution is proposed. This paper describes a mathematical model for simulation of the temperature evolution inside a space with thermally insulated walls under selected outside conditions. Computer simulations are then used to assess the temperature distribution inside the room and the heat flow through the room walls. Results of the simulations are used for subsequent determination of the time needed for the desired decrease of air temperature inside the tested room during its cooling due to the low ambient temperature, which is related to the thermal stability of the building, specific heat capacity, and thickness of the thermal insulation. Under the studied conditions, the time to reach the temperature drops by 20 percent in a room with windows was from 1.4 to 1.8 times lower than that in the room without windows. The proposed methodology shows the flexibility of computer modelling in the design of insulated building systems. The mesh density testing was performed by comparing the air temperature evolution in the model of the selected mesh density and the model with its maximum value enabled by the size of computer memory. The maximum temperature deviation calculated for the mesh of the presented model was 0.57%.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

2018

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“…Emotion detection research divides two emotions, namely hate and non-hate. Besides, the distribution of feelings is also divided into three types: strong hate, weak hate, and no hate [19]. In its identification, this study analyzes emotions in detecting fanaticism in the document text.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Research in sentiment analysis can detect hate speech based on data from Facebook. This research belongs to unstructured sentiment analysis [19]. The application of the Support Vector Machines (SVM) method and a particular Recurrent Neural Network, namely Long Short Term Memory (LSTM), were tested for performance in emotion recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

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Testing of Method for Assessing of Room Thermal Stability

Cited by 2 publications

References 1 publication

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

Computer Simulation of Temperature Distribution during Cooling of the Thermally Insulated Room

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

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