The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures
To optimize the backfilling of ground source heat pump drilling mud and boost the thermal conductivity of drilling materials, this study proposes using a mixture of limestone sand and loess, typical in karst regions, as backfill for buried pipe heat exchangers. Through indoor experiments, 152 limest...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25008056 |
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| author | Xiong Liu Ruiyong Mao Zujing Zhang Hongwei Wu Xing Liang Jing Chen |
| author_facet | Xiong Liu Ruiyong Mao Zujing Zhang Hongwei Wu Xing Liang Jing Chen |
| author_sort | Xiong Liu |
| collection | DOAJ |
| description | To optimize the backfilling of ground source heat pump drilling mud and boost the thermal conductivity of drilling materials, this study proposes using a mixture of limestone sand and loess, typical in karst regions, as backfill for buried pipe heat exchangers. Through indoor experiments, 152 limestone sand-loess mixtures were prepared and their thermal conductivities tested. Analyses explored the impacts of limestone sand content, moisture content, dry density, and particle size distribution. Results show that artificially graded materials generally outperform natural ones in thermal conductivity, with grading's influence decreasing as moisture rises. At 8 % moisture, grading increases thermal conductivity by 18.57 % (0.069–0.124 W/(m·K)); at 20 %, the increase is 7.63 %. High moisture and limestone sand content can yield a thermal conductivity of 1.508 W/(m·K). When using graded materials, geological conditions and aquifers should be considered, and they suit strata with moderate moisture. A backpropagation neural network - based predictive model for thermal conductivity, developed from experimental data, achieved 6.4 % average absolute percentage error, indicating good accuracy. |
| format | Article |
| id | doaj-art-4e8b71a4576b46c3b5e9f28792e66b77 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-4e8b71a4576b46c3b5e9f28792e66b772025-08-20T03:47:19ZengElsevierCase Studies in Thermal Engineering2214-157X2025-09-017310654510.1016/j.csite.2025.106545The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixturesXiong Liu0Ruiyong Mao1Zujing Zhang2Hongwei Wu3Xing Liang4Jing Chen5College of Civil Engineering, Guizhou Provincial Key Laboratory of Rock and Soil Mechanics and Engineering Safety, Guizhou University, Guiyang, 550025, ChinaCollege of Civil Engineering, Guizhou Provincial Key Laboratory of Rock and Soil Mechanics and Engineering Safety, Guizhou University, Guiyang, 550025, China; Corresponding author.College of Civil Engineering, Guizhou Provincial Key Laboratory of Rock and Soil Mechanics and Engineering Safety, Guizhou University, Guiyang, 550025, ChinaSchool of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UKSchool of Computer Science and Mathematics, Kingston University London, KT1 2EE, UKGuizhou Polytechnic of Construction, Guiyang, 550025, ChinaTo optimize the backfilling of ground source heat pump drilling mud and boost the thermal conductivity of drilling materials, this study proposes using a mixture of limestone sand and loess, typical in karst regions, as backfill for buried pipe heat exchangers. Through indoor experiments, 152 limestone sand-loess mixtures were prepared and their thermal conductivities tested. Analyses explored the impacts of limestone sand content, moisture content, dry density, and particle size distribution. Results show that artificially graded materials generally outperform natural ones in thermal conductivity, with grading's influence decreasing as moisture rises. At 8 % moisture, grading increases thermal conductivity by 18.57 % (0.069–0.124 W/(m·K)); at 20 %, the increase is 7.63 %. High moisture and limestone sand content can yield a thermal conductivity of 1.508 W/(m·K). When using graded materials, geological conditions and aquifers should be considered, and they suit strata with moderate moisture. A backpropagation neural network - based predictive model for thermal conductivity, developed from experimental data, achieved 6.4 % average absolute percentage error, indicating good accuracy.http://www.sciencedirect.com/science/article/pii/S2214157X25008056Ground source heat pumpBackfill materialGradationSand-soil mixtureBP neural network prediction |
| spellingShingle | Xiong Liu Ruiyong Mao Zujing Zhang Hongwei Wu Xing Liang Jing Chen The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures Case Studies in Thermal Engineering Ground source heat pump Backfill material Gradation Sand-soil mixture BP neural network prediction |
| title | The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures |
| title_full | The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures |
| title_fullStr | The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures |
| title_full_unstemmed | The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures |
| title_short | The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures |
| title_sort | thermal conductivity characteristics and prediction models of limestone sand yellow soil mixtures |
| topic | Ground source heat pump Backfill material Gradation Sand-soil mixture BP neural network prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25008056 |
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