Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network. (27th March 2021)
- Record Type:
- Journal Article
- Title:
- Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network. (27th March 2021)
- Main Title:
- Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network
- Authors:
- Ren, Xiuling
You, Yanhui
Yu, Qihao
Zhang, Guike
Yue, Pan
Jin, Mingyang - Other Names:
- Xu Xiangtian Academic Editor.
- Abstract:
- Abstract : Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in theAbstract : Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process. … (more)
- Is Part Of:
- Advances in materials science and engineering. Volume 2021(2021)
- Journal:
- Advances in materials science and engineering
- Issue:
- Volume 2021(2021)
- Issue Display:
- Volume 2021, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 2021
- Issue:
- 2021
- Issue Sort Value:
- 2021-2021-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-03-27
- Subjects:
- Materials science -- Periodicals
Materials science
Periodicals
620.11 - Journal URLs:
- http://www.hindawi.com/journals/amse ↗
- DOI:
- 10.1155/2021/5555565 ↗
- Languages:
- English
- ISSNs:
- 1687-8434
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 16202.xml