Thermal conductivity prediction model for compacted bentonites considering temperature variations
An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-10-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573321002527 |
| _version_ | 1818645728600260608 |
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| author | Seok Yoon Min-Jun Kim Seunghun Park Geon-Young Kim |
| author_facet | Seok Yoon Min-Jun Kim Seunghun Park Geon-Young Kim |
| author_sort | Seok Yoon |
| collection | DOAJ |
| description | An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 °C to 80–90 °C. As a result, thermal conductivity increased by 5–20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R2 value of the regression model was 0.925, and the regression model and metamodel showed similar results. |
| first_indexed | 2024-12-17T00:35:21Z |
| format | Article |
| id | doaj.art-d42779ec41c0424dbcb5aa919cf0c2c8 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-17T00:35:21Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-d42779ec41c0424dbcb5aa919cf0c2c82022-12-21T22:10:10ZengElsevierNuclear Engineering and Technology1738-57332021-10-01531033593366Thermal conductivity prediction model for compacted bentonites considering temperature variationsSeok Yoon0Min-Jun Kim1Seunghun Park2Geon-Young Kim3Radioactive Waste Disposal Research Division, KAERI, Daejeon, 34057, South Korea; Corresponding author.Deep Subsurface Research Center, KIGAM, Daejeon, 34132, South KoreaDepartment of Energy Resource Engineering, Inha University, Incheon, 22212, South KoreaRadioactive Waste Disposal Research Division, KAERI, Daejeon, 34057, South KoreaAn engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 °C to 80–90 °C. As a result, thermal conductivity increased by 5–20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R2 value of the regression model was 0.925, and the regression model and metamodel showed similar results.http://www.sciencedirect.com/science/article/pii/S1738573321002527Compacted bentoniteThermal conductivityTemperature variationMultiple regression analysisMetamodel |
| spellingShingle | Seok Yoon Min-Jun Kim Seunghun Park Geon-Young Kim Thermal conductivity prediction model for compacted bentonites considering temperature variations Nuclear Engineering and Technology Compacted bentonite Thermal conductivity Temperature variation Multiple regression analysis Metamodel |
| title | Thermal conductivity prediction model for compacted bentonites considering temperature variations |
| title_full | Thermal conductivity prediction model for compacted bentonites considering temperature variations |
| title_fullStr | Thermal conductivity prediction model for compacted bentonites considering temperature variations |
| title_full_unstemmed | Thermal conductivity prediction model for compacted bentonites considering temperature variations |
| title_short | Thermal conductivity prediction model for compacted bentonites considering temperature variations |
| title_sort | thermal conductivity prediction model for compacted bentonites considering temperature variations |
| topic | Compacted bentonite Thermal conductivity Temperature variation Multiple regression analysis Metamodel |
| url | http://www.sciencedirect.com/science/article/pii/S1738573321002527 |
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