Soil Thermal Conductivity Models Research Articles

Assessment of some soil thermal conductivity models via variations in temperature and bulk density at low moisture range

Simulation of heat transfer in soil under steady and unsteady situations requires reliable estimate of soil thermal conductivity (λ) at varying environmental conditions. In the current work several soil thermal conductivity predicting models including I) de Vries, II) Campbell, III) combined de Vries and Campbell and IV) de Vries-Nobre were evaluated for the four soils of coarse sand, sandy loam, loam and clay loam textured at varying in temperature and bulk density at low moisture range. Thermal conductivities measured by the cylindrical probe method served as the reference for models assessment. Results showed that approximately same thermal conductivities obtained by the five methods at low moisture range (θ ≤ 0.05 m3/m3). Also the de Vries and de Vries-Campbell models produced accurate than Campbell and de vries-Nobre models. The accuracy of the two models increased with soil compaction but decreased with temperature rise. Campbell model showed more reliability at higher (311.16 and 321.16 K) temperatures; but its accuracy declined with soil compaction in current work. It seems that assuming needle shape for the soil particles is far away from the reality whereas assuming spherical shapes may be more realistic and produced more satisfactory prediction of thermal conductivity. The compaction would alter particle arrangement and may increase the contact area of particles; and then make them behave more or less spherical shape.it seems thermal conductivity in solid particles increase via increasing in temperature. Since a modified mineral shape factor, gm, was developed as a combination between sphere and needle according to geometric mean particle diameter as well as bulk density and temperature as modifying factors. This factor increased the accuracy of de Vries-Nobre model up to 10.37%. Regarding nonlinear regression model, moisture content, bulk density, temperature and quartz content demonstrated significant effect on soil thermal conductivity in our investigation.

Evaluation of soil thermal conductivity models

Numerous models have been developed to predict the thermal conductivity of soils at a range of different densities and moisture contents. This paper evaluates four thermal conductivity models, developed by various researchers, by comparing their performance against experimental results obtained on 27 different soils prepared at a range of saturation levels and densities. The results demonstrate that, in general, all four models show good agreement between experimental thermal conductivity and modelled thermal conductivity. The only significant shortfall is observed in low-saturated sands when using two of the models. A detailed analysis of the empirical soil parameters used in three of the recent models is presented. It shows that the accuracy of the three models can be improved by modifying the empirical soil parameters to fit the experimental data.

Experimental Investigation of Soil Thermal Conductivity Over a Wide Temperature Range

The results are reported of an experimental investigation of the soil thermal conductivity over a wide temperature range, for various water contents and two soil types. The results are particularly important in predictions of underground heat transfer, which require a quantitative understanding of the coupled dependence of the soil thermal conductivity on texture, temperature, and water content. In the research, comprehensive sets of thermal conductivity for Ottawa sand (coarse soil) and Richmond Hill fine sandy loam (medium soil) are experimentally obtained using the guarded hot-plate method, for temperatures ranging from \(2\,^{\circ }\mathrm{C}\) to \(92\,^{\circ }\mathrm{C}\) and water contents varying from complete dryness to full saturation. For both soils, the thermal conductivity is observed to vary in three stages with respect to increasing water content: a very minor increase as water content increases to the permanent wilting point, a steep increase as water content further increases to field capacity, and a minor increase (for temperatures less than \(72\,^{\circ }\mathrm{C}\)) or decrease for (temperatures greater than \(72\,^{\circ }\mathrm{C}\)) when the field capacity is exceeded. Then, on the basis of gathered datasets, a similar \(Ke(S_{\mathrm{r}},T)\) form of the soil thermal conductivity model by Tarnawski et al. is used to empirically fit the data. The resulted correlations fit the data well with their overall root-relative-mean-square percentage errors of 4.7 % and 6.1 % for Ottawa sand and Richmond Hill fine sandy loam, respectively, and are suitable for most engineering applications.

The Primary and Secondary Analysis of Uncertain Factors in Soil Thermal Properties for GSHP

In this paper, at first, an effective soil thermal conductivity model was established. Single factor regression analysis for 6 uncertain factors contained in the model was then conducted respectively. Finally, the primary and secondary characters of these uncertain factors were analyzed by using the orthogonal test. The analysis results show that the effective soil thermal conductivity has linear relationships with the saturation degree of unsaturated soil and the depth of water table and has power function relationships with other 4 uncertain factors; the porosity of unsaturated soil has the greatest effect on the effective soil thermal properties, followed by saturation degree of unsaturated soil, porosity of saturated soil, solid phase thermal conductivity of unsaturated soil, solid phase thermal conductivity of saturated soil and the depth of water table.

Extension of soil thermal conductivity models to frozen meats with low and high fat content

Thermal conductivity models of frozen soils were analyzed and compared with similar models developed for frozen foods. In total, eight thermal conductivity models and 54 model versions were tested against experimental data of 13 meat products in the temperature range from 0 to −40 °C. The model by deVries, with water+ice ( wi) as the continuous phase, showed overall the best predictions. The use of wi leads generally to improved predictions in comparison to ice; water as the continuous phase is beneficial only to deVries model, mostly from −1 to −20 °C; fat is advantageous only to meats with high fat content. The results of this work suggest that the more sophisticated way of estimating the thermal conductivity for a disperse phase in the deVries model might be more appropriate than the use of basic multi-phase models (geometric mean, parallel, and series). Overall, relatively small differences in predictions were observed between the best model versions by deVries, Levy, Mascheroni, Maxwell or Gori as applied to frozen meats with low content of fat. These differences could also be generated by uncertainty in meat composition, temperature dependence of thermal conductivity of ice, measurement errors, and limitation of predictive models.