Inverse estimation of rock thermal conductivity based on numerical microscale modeling from sandstone thin sections

2017 ◽  
Vol 231 ◽  
pp. 1-8 ◽  
Author(s):  
Katharina Albert ◽  
Claudia Franz ◽  
Roland Koenigsdorff ◽  
Kai Zosseder
Keyword(s):  
Thermal Conductivity ◽  
Thin Sections ◽  
Inverse Estimation ◽  
Microscale Modeling ◽  
Rock Thermal Conductivity

Cure of GR-S in Thick Articles

1944 ◽  
Vol 17 (4) ◽  
pp. 984-993
Author(s):  
Ross E. Morris ◽  
Joseph W. Hollister ◽  
Paul A. Mallard
Keyword(s):  
Thermal Conductivity ◽  
Heat Flow ◽  
Laboratory Tests ◽  
Exothermic Reaction ◽  
Thin Sheets ◽  
Thin Sections ◽  
Curing Conditions ◽  
Know How ◽  
Flow Through ◽  
The Difference

Abstract Information on the relative rates of cure of GR-S stocks and similar Hevea stocks in thick sections is of interest to many rubber manufacturers. Since curing conditions for thick articles from Hevea stocks have been established, they would like to know how these conditions must be altered when GR-S stocks are used in the same applications. They could develop a GR-S stock with the same rate of cure as the Hevea stock which it replaces according to laboratory tests on comparatively thin sheets, but this agreement does not mean necessarily that thick sections cure at the same rate. The respective rates of heat flow through the rubbers must be considered. Only if the rates of heat flow as well as the curing rates of thin sections are in agreement, will the curing rates of the thick sections be equal. Juve and Garvey found that GR-S tread stocks cure faster in the center of thick sections than similar Hevea tread stocks. They were unable to explain this behavior because, according to their measurements, the thermal conductivity of the GR-S tread stock was less, and its specific heat greater than, the corresponding values for the Hevea tread stock. They concluded that the difference mav be due to an exothermic reaction.

Portableinsituwall‐rock thermal conductivity meter for mine pits

1991 ◽  
Vol 62 (6) ◽  
pp. 1581-1586 ◽  
Author(s):  
Xian‐jie Shen ◽  
Shu‐zhen Yang ◽  
Wen‐ren Zhang
Keyword(s):  
Thermal Conductivity ◽  
Rock Thermal Conductivity

Thermal conductivity estimation model considering the effect of water saturation explaining the heterogeneity of rock thermal conductivity

Geothermics ◽  
2017 ◽  
Vol 66 ◽  
pp. 1-12 ◽  
Author(s):  
Katharina Albert ◽  
Marcellus Schulze ◽  
Claudia Franz ◽  
Roland Koenigsdorff ◽  
Kai Zosseder
Keyword(s):  
Thermal Conductivity ◽  
Water Saturation ◽  
Estimation Model ◽  
Effect Of Water ◽  
Rock Thermal Conductivity

Analysis of Impact of Moisture Content on Rock Thermal Conductivity Coefficient Using TCS Method

2015 ◽  
Vol 1095 ◽  
pp. 429-432 ◽  
Author(s):  
Zi Wang Yu ◽  
Yan Jun Zhang ◽  
Ping Gao
Keyword(s):  
Thermal Conductivity ◽  
Moisture Content ◽  
Geothermal Energy ◽  
Thermal Conductivity Coefficient ◽  
Reference Value ◽  
Coefficient Of Thermal Conductivity ◽  
Relative Thermal Conductivity ◽  
Structure Density ◽  
Rock Thermal Conductivity ◽  
Thermal Conductivity Of Rock

The coefficient of thermal conductivity scanner (TCS) was used to test granodiorite, sandstone and rhyolite samples, focuses on the changing rule of the thermal conductivity coefficient of rock under different moisture content. The coefficient of thermal conductivity of the rock increases with water content, and follow a linear relationship. The relative thermal conductivity of three kinds of rock sample is: granodiorite higher than sandstone and higher than rhyolite. The higher the structure density at the same time, the smaller the porosity, the stronger the cementation, the higher the strength, the greater the thermal conductivity of rock mass. This conclusion can be used with geothermal energy development, and has certain reference value.

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