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 Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

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 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.

Modelling of frozen soil thermal conductivity

2020 ◽  
Author(s):  
Hailong He ◽  
Dong He ◽  
Yuki Kojima ◽  
Gerald Flerchinger ◽  
Miles Dyck
Keyword(s):  
Thermal Conductivity ◽  
Phase Change ◽  
Transport Processes ◽  
Frozen Soil ◽  
Unfrozen Water ◽  
Extensive Literature ◽  
Soil Thermal Conductivity ◽  
Differential Heat ◽  
Steady State Method ◽  
Water Redistribution

<p>Frozen soil thermal conductivity (FSTC), which describes frozen soils’ ability to conduct heat under a unit temperature gradient, is a critical parameter of the partial differential heat conduction equation required for numerical studies of coupled heat and mass transport processes and engineering applications in cold and arid regions. FSTC is complicated because it is affected by factors such as temperature, unfrozen water and ice content, and soil texture. Although many FSTC models are available in literature, many of these models were developed using steady-state method that are subject to errors associated with phase change and water redistribution or not even tested with experiments. In addition, no studies have assessed their applicability and reliability. We conducted an extensive literature review and collated over 30 FSTC models. Their performance was evaluated with a large compiled dataset measured with transient method (e.g., heat pulse method), which is less likely to be affected by phase change and water redistribution at unfrozen or low subfreezing temperatures. In addition, a new FSTC model that is capable of accurately estimating FSTC at both unfrozen and frozen conditions is proposed.</p>

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