Work

The thermal conductivity characteristics and prediction models of limestone sand-yellow soil mixtures

Year: 2025

Type: article

Abstract: 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.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.106545

Authors: Xiong Liu, Ruiyong Mao, Zujing Zhang, Hongwei Wu, Xing Liang, Jing Chen

Institutions: Guizhou University, University of Hertfordshire, Kingston University London, Guizhou Polytechnic of Construction

Language: English

Topic: Geothermal Energy Systems and Applications

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,620

Funders: National Natural Science Foundation of China, Science and Technology Program of Guizhou Province