Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A new thermal conductivity estimation model for weathered granite soils in Korea

  • Go, Gyu-Hyun (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Lee, Seung-Rae (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Kim, Young-Sang (Department of Marine and Civil Engineering, Chonnam National University) ;
  • Park, Hyun-Ku (Engineering & Construction Group Civil Engineering Division, Samsung C&T) ;
  • Yoon, Seok (Department of Civil Engineering, Korean Advanced Institute for Science and Technology)
  • Received : 2013.06.20
  • Accepted : 2013.11.19
  • Published : 2014.04.25
⟨ Previous Next ⟩

Abstract

Thermal conductivity of ground has a great influence on the performance of Ground Heat Exchangers (GHEs). In general, the ground thermal conductivity significantly depends on the density (or porosity) and the moisture content since they are decisive factors that determine the interface area between soil particles which is available for heat transfer. In this study, a large number of thermal conductivity experiments were conducted for soils of varying porosity and moisture content, and a database of thermal properties for the weathered granite soils was set up. Based on the database, a 3D Curved Surface Model and an Artificial Neural Network Model (ANNM) were proposed for estimating the thermal conductivity. The new models were validated by comparing predictions by the models with new thermal conductivity data, which had not been used in developing the models. As for the 3D CSM, the normalized average values of training and test data were 1.079 and 1.061 with variations of 0.158 and 0.148, respectively. The predictions became somewhat unreliable in a low range of thermal conductivity values in considering the distribution pattern. As for the ANNM, the 'Logsig-Tansig' transfer function combination with nine neurons gave the most accurate estimates. The normalized average values of training data and test data were 1.006 and 0.954 with variations of 0.026 and 0.098, respectively. It can be concluded that the ANNM gives much better results than the 3D CSM.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea, Korea Institute of Construction and Transportation Technology Evaluation and Planning

References

  1. Bennett, M. (2008), The Ultimate Ground Source Heat Pump Guide - Part 4, Geothermal Energy, ZIMBIO, December.
  2. Brandle, H. (2006), "Energy foundations and other thermo-active ground structures", Geotech., 56(2), 81-122. https://doi.org/10.1680/geot.2006.56.2.81
  3. Cote, J. and Konrad, J.M. (2005), "A generalized thermal conductivity model for soils and construction materials", Can. Geotech. J., 42(2), 443-458. https://doi.org/10.1139/t04-106
  4. Demuth, H. and Beale, M. (1992), User's Guide of Neural Network Toolbox for Use with MATLAB, The Mathworks, Natick, USA.
  5. Farouki, O.T. (1986), Thermal Properties of Soils; Series on Rock and Soil Mechanics, Volume 11, Trans Tech Publications.
  6. Hart, G.K. and Whiddon, W.I. (1984), "Ground source heat pump planning workshop, Summary of proceedings", EPRI Report RP 2033-12, Palo Alto: Electric Power Research Institute.
  7. Horai, K. (1971), "Thermal conductivity of rock-forming minerals", J. Geophys. Res., 76(5), 1278-1307. https://doi.org/10.1029/JB076i005p01278
  8. Hukseflux (2006), User Manual of TP08: Small size non-steady probe for thermal conductivity measurement, Hukseflux Thermal Sensors, Delft, Netherlands.
  9. Johansen, O. (1975), "Thermal conductivity of soils", Ph.D. Thesis, University of Trondheim, Norway.
  10. Kersten, M.S. (1949), Laboratory Research for the Determination of the Thermal Properties of Soils, Research Laboratory Instigations, Engineering Experiment Station, University of Minnesota, Minneapolis, MI, USA, Tech. Report 23.
  11. Kim, Y.S. (2002), "Feasibility of neural network model application for determination of preconsolidation pressure of soft deposit by piezocone test", J. KSCE., 22(6), 623-633.
  12. Kim, Y.S. (2005), "Development of neural network nodel for estimation of undrained shear strength of Korean soft soil based on UU triaxial test and piezocone test results", J. KGS., 21(8), 73-84.
  13. Laloui, L., Moreni, L. and Vulliet, L. (2003), "Behavior of a dual-purpose pile as foundation and heat exchanger", Can. Geotech. J., 40(2), 388-402. https://doi.org/10.1139/t02-117
  14. Lee, G.J. (2010), "Study on thermal characteristics of backfill materials for horizontal ground heat exchanger", Master Thesis, Korea University, Korea.
  15. Lee, Y.G., Yoon, Y.W. and Kang, B.H. (2000), "Prediction of undrained shear strength of normally consolidated clay with varying consolidation pressure ratios using artificial neural networks", J. KGS., 16(1), 75-81.
  16. Lu, S., Ren, T. and Gong, Y. (2007), "An improved model for predicting soil thermal conductivity from water content at room temperature", SSSA J., 71(1), 8-14. https://doi.org/10.2136/sssaj2006.0041
  17. Min, T.K., Hwang, K.M. and Jeon, H.W. (2000), "Prediction of consolidation settlements at vertical drain using modular artificial neural networks", J. KGS., 16(2), 71-77.
  18. Misra, A. and Huang, S. (2011), "Effect of loading induced anisotropy on the shear behavior of rough interfaces", Tribology Int., 44(5), 627-634. https://doi.org/10.1016/j.triboint.2010.12.010
  19. Netzsch (2013), Heat Flow Meters HFM 436 Lambda series, Netzsch, Selb, Germany.
  20. Olgun, C.G., Martin, J.R. and Bowers, G.A. (2012), Energy Piles: Using Deep Foundations as Heat Exchangers, Geo-Strata, March/April.
  21. Pahud, D., Fromentin, A. and Hubbuch, M. (1999), "Heat exchanger pile system for heating and cooling at Zurich Airport", IEA Heat Pump Centre Newsletter, 17(1), 15-16.
  22. Park, H.S. (2011), "Thermal conductivities of unsaturated Korean weathered granite soils", Master Thesis, Korea Advanced Institute of Science and Technology, Korea.
  23. Park, H.K., Park, H.S., Lee, S.R. and Go, G.H. (2012), "Estimation of thermal conductivity of weathered granite soils", J. KSCE., 32(2), 69-77.
  24. Penner, E., Johnston, G.H. and Goodrich, L.E. (1975), "Thermal conductivity laboratory studies of some MacKenzie highway soils", Can. Geotech. J., 12(3), 271-288. https://doi.org/10.1139/t75-033
  25. Salomone, L.A. and Kovacs, W.D. (1984), "Thermal resistivity of soils", J. Geotech. Eng., 110(3), 375-389. https://doi.org/10.1061/(ASCE)0733-9410(1984)110:3(375)

Cited by

  1. Investigation of the effect of seasonal variation in ground temperature on Thermal Response Tests 2017, https://doi.org/10.1016/j.renene.2017.12.095
  2. Estimation of saturated hydraulic conductivity of Korean weathered granite soils using a regression analysis vol.9, pp.1, 2015, https://doi.org/10.12989/gae.2015.9.1.101
  3. Experimental investigation on the variation of thermal conductivity of soils with effective stress, porosity, and water saturation vol.11, pp.6, 2016, https://doi.org/10.12989/gae.2016.11.6.771
  4. Use of a Pre-Drilled Hole for Implementing Thermal Needle Probe Method for Soils and Rocks vol.9, pp.12, 2016, https://doi.org/10.3390/en9100846
  5. A predicting model for thermal conductivity of high permeability-high strength concrete materials vol.10, pp.1, 2016, https://doi.org/10.12989/gae.2016.10.1.049
  6. Evaluation of calculation models for the thermal conductivity of soils vol.94, pp.None, 2014, https://doi.org/10.1016/j.icheatmasstransfer.2018.02.005
  7. Thermographic analysis of failure for different rock types under uniaxial loading vol.23, pp.6, 2020, https://doi.org/10.12989/gae.2020.23.6.503
  8. A review and evaluation of thermal conductivity models of saturated soils vol.67, pp.7, 2014, https://doi.org/10.1080/03650340.2020.1771315