An evaluation of soil thermal conductivity models based on the porosity and degree of saturation and a proposal of a new improved model

2021 ◽  
Vol 129 ◽  
pp. 105738
Author(s):  
Weidong Zhang ◽  
Ruiqiang Bai ◽  
Xiangtian Xu ◽  
Wei Liu
Keyword(s):  
Thermal Conductivity ◽  
Degree Of Saturation ◽  
Soil Thermal Conductivity ◽  
Improved Model ◽  
Conductivity Models

Thermal conductivity of base-course materials

2005 ◽  
Vol 42 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Jean Côté ◽  
Jean-Marie Konrad
Keyword(s):  
Thermal Conductivity ◽  
Prediction Models ◽  
Geometric Mean ◽  
Coarse Sand ◽  
Solid Particles ◽  
Degree Of Saturation ◽  
Empirical Prediction ◽  
Course Materials ◽  
Base Course ◽  
Improved Model

This paper presents the results of a comprehensive laboratory study on the thermal conductivity of dense and broadly graded coarse base-course materials used in pavements. Materials were selected from eight quarries along the axis of the St. Lawrence River to include a variety of samples of different geological origins. Nearly 200 tests were performed in a thermal conductivity cell using Pyrex heat flux meters to characterize the relationships between the thermal conductivity of unfrozen and frozen samples and the water–ice content. Sixteen tests were also performed on solid rock cylinders to characterize the influence of mineralogy on the thermal conductivity of solid particles from the selected quarries. The most widely used empirical prediction models for thermal conductivity of soils from the literature were found inappropriate to estimate the thermal conductivity of base-course materials. An improved model using the geometric mean method to compute the thermal conductivity for the solid particles and the saturated materials, a modified form of the geometric mean method to predict the thermal conductivity of dry materials, and empirical relationships to assess the normalized thermal conductivity of unfrozen and frozen base-course materials are presented. This new model predicted well the thermal conductivity for more than 150 unfrozen and frozen coarse sand and gravel samples from the literature. A step by step methodology is proposed to assess the thermal conductivity of base-course materials.Key words: base course, porosity, degree of saturation, mineralogy, unfrozen–frozen, thermal conductivity.

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

2005 ◽  
Vol 28 (6) ◽  
pp. 840-850 ◽  
Author(s):  
V.R. Tarnawski ◽  
D.J. Cleland ◽  
S. Corasaniti ◽  
F. Gori ◽  
R.H. Mascheroni
Keyword(s):  
Thermal Conductivity ◽  
Fat Content ◽  
Soil Thermal Conductivity ◽  
High Fat ◽  
Conductivity Models

Evaluation of soil thermal conductivity models

2015 ◽  
Vol 52 (11) ◽  
pp. 1892-1900 ◽  
Author(s):  
D. Barry-Macaulay ◽  
A. Bouazza ◽  
B. Wang ◽  
R.M. Singh
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Detailed Analysis ◽  
Soil Parameters ◽  
Soil Thermal Conductivity ◽  
Moisture Contents ◽  
Saturated Sands ◽  
Good Agreement ◽  
Different Soils ◽  
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.

scholarly journals Evaluation of 14 frozen soil thermal conductivity models with observations and SHAW model simulations

Geoderma ◽  
2021 ◽  
Vol 403 ◽  
pp. 115207
Author(s):  
Hailong He ◽  
Gerald N. Flerchinger ◽  
Yuki Kojima ◽  
Dong He ◽  
Stuart P. Hardegree ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Frozen Soil ◽  
Soil Thermal Conductivity ◽  
Model Simulations ◽  
Conductivity Models

scholarly journals Temperature Changes in the Vicinity of Thermally Loaded Structure Embedded in the Soil: Effect of Sand Content and Saturation Degree

2017 ◽  
Vol 39 (2) ◽  
pp. 61-71
Author(s):  
Adrian Różański
Keyword(s):  
Thermal Conductivity ◽  
Solid Phase ◽  
Rapid Development ◽  
Degree Of Saturation ◽  
Soil Thermal Conductivity ◽  
Soil Medium ◽  
Temperature Changes ◽  
Soil Skeleton ◽  
Soil Conductivity ◽  
Loaded Structure

Abstract Due to the rapid development of geothermal technologies, the problem of efficient and proper evaluation of soil thermal conductivity becomes extremely important. Factors mostly affecting the soil conductivity are the conductivity of solid phase and the degree of saturation. The former one is mainly affected by the mineral composition, in particular, by the content of quartz whose conductivity is the highest one among all the minerals forming soil skeleton. Organic matter, because of its relatively low conductivity, influences the solid conductivity as well. The problem addressed in the paper is the influence of mentioned factors on temperature changes in the vicinity of thermally loaded structure embedded in the soil medium. Numerical simulations are carried out for different values of soil thermal conductivity resulting from various quartz contents and degrees of saturation. In addition, a weak coupled - heat and water transport - problem is considered.

scholarly journals Effective thermal conductivity of unsaturated soils based on deep learning algorithm

2020 ◽  
Vol 205 ◽  
pp. 04006
Author(s):  
Zarghaam Haider Rizvi ◽  
Syed Jawad Akhtar ◽  
Wurood Talib Sabeeh ◽  
Frank Wuttke
Keyword(s):  
Thermal Conductivity ◽  
Deep Learning ◽  
Effective Thermal Conductivity ◽  
Unsaturated Soils ◽  
Learning Algorithm ◽  
Critical Role ◽  
Degree Of Saturation ◽  
Ann Model ◽  
Soil Thermal Conductivity ◽  
Validation Data

Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy.

scholarly journals Impact of gravels and organic matter on the thermal properties of grassland soils in southern France

2015 ◽  
Vol 2 (1) ◽  
pp. 737-765
Author(s):  
J.-C. Calvet ◽  
N. Fritz ◽  
C. Berne ◽  
B. Piguet ◽  
W. Maurel ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Organic Matter ◽  
Soil Moisture ◽  
Soil Organic Matter ◽  
Soil Temperature ◽  
Soil Thermal Conductivity ◽  
Volumetric Fraction ◽  
Southern France ◽  
The Impact ◽  
Conductivity Models

Abstract. Soil moisture is the main driver of temporal changes in values of the soil thermal conductivity. The latter is a key variable in land surface models (LSMs) used in hydrometeorology, for the simulation of the vertical profile of soil temperature in relation to soil moisture. Shortcomings in soil thermal conductivity models tend to limit the impact of improving the simulation of soil moisture in LSMs. Models of the thermal conductivity of soils are affected by uncertainties, especially in the representation of the impact of soil properties such as the volumetric fraction of quartz (q), soil organic matter, and gravels. As soil organic matter and gravels are often neglected in LSMs, the soil thermal conductivity models used in most LSMs represent the mineral fine earth, only. Moreover, there is no map of q and it is often assumed that this quantity is equal to the volumetric fraction of sand. In this study, q values are derived by reverse modelling from the continuous soil moisture and soil temperature sub-hourly observations of the Soil Moisture Observing System – Meteorological Automatic Network Integrated Application (SMOSMANIA) network at 21 grassland sites in southern France, from 2008 to 2015. The soil temperature observations are used to retrieve the soil thermal diffusivity (Dh) at a depth of 0.10 m in unfrozen conditions, solving the thermal diffusion equation. The soil moisture and Dh values are then used together with the measured soil properties to retrieve soil thermal conductivity (λ) values. For ten sites, the obtained λ value at saturation (λsat) cannot be retrieved or is lower than the value corresponding to a null value of q, probably in relation to a high density of grass roots at these sites or to the presence of stones. For the remaining eleven sites, q is negatively correlated with the volumetric fraction of solids other than sand. The impact of neglecting gravels and organic matter on λsat is assessed. It is shown that these factors have a major impact on λsat.

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