Assessing the impact of quartz content on the prediction of soil thermal conductivity

Géotechnique ◽  
2009 ◽  
Vol 59 (4) ◽  
pp. 331-338 ◽  
Author(s):  
V. R. Tarnawski ◽  
T. Momose ◽  
W. H. Leong
Keyword(s):  
Thermal Conductivity ◽  
Soil Thermal Conductivity ◽  
Quartz Content ◽  
The Impact

scholarly journals On parameterization of heat conduction in coupled soil water and heat flow modelling

2012 ◽  
Vol 7 (No. 4) ◽  
pp. 125-137 ◽  
Author(s):  
J. Votrubová ◽  
M. Dohnal ◽  
T. Vogel ◽  
M. Tesař
Keyword(s):  
Thermal Conductivity ◽  
Soil Water ◽  
Heat Transport ◽  
Preferential Flow ◽  
Thermal Conditions ◽  
Industrial Applications ◽  
Soil Thermal Conductivity ◽  
Vegetation Season ◽  
Simulation Results ◽  
The Impact

Soil water and heat transport plays an important role in various hydrologic, agricultural, and industrial applications. Accordingly, an increasing attention is paid to relevant simulation models. In the present study, soil thermal conditions at a mountain meadow during the vegetation season were simulated. A dual-continuum model of coupled water and heat transport was employed to account for preferential flow effects. Data collected at an experimental site in the Šumava Mountains, southern Bohemia, during the vegetation season 2009 were employed. Soil hydraulic properties (retention curve and hydraulic conductivity) determined by independent soil tests were used. Unavailable hydraulic parameters were adjusted to obtain satisfactory hydraulic model performance. Soil thermal properties were estimated based on values found in literature without further optimization. Three different approaches were used to approximate the soil thermal conductivity function, λ(θ): (i) relationships provided by Chung and Horton (ii) linear estimates as described by Loukili, Woodbury and Snelgrove, (iii) methodology proposed by Côté and Konrad. The simulated thermal conditions were compared to those observed. The impact of different soil thermal conductivity approximations on the heat transport simulation results was analysed. The differences between the simulation results in terms of the soil temperature were small. Regarding the surface soil heat flux, these differences became substantial. More realistic simulations were obtained using λ(θ) estimates based on the soil texture and composition. The differences between these two, related to neglecting vs. considering λ(θ) non-linearity, were found negligible.

scholarly journals Experimental And Numerical Invetigation Of Dependence Of Soil Thermal Conductivity On Temperature Water Content, And Soil Texture

2021 ◽  
Author(s):  
Behnam Jowkar-Baniani
Keyword(s):  
Thermal Conductivity ◽  
Water Content ◽  
Soil Texture ◽  
Conductivity Data ◽  
Soil Thermal Conductivity ◽  
Pump System ◽  
Heat Pump System ◽  
Thermal Conductivity Data ◽  
Apparent Thermal Conductivity ◽  
The Impact

Comprehensive set of thermal conductivity data for a loam soil was generated, for temperature variations from 5ºC to 92ºC and water content variations from dry to saturation, and compared to two other soil textures. The results exhibited similar characteristics as those of the other textures, where a significant change in soil thermal conductivity was. Using the thermal conductivity data sets, a model representing heat and mass transfer in soil was used to study the apparent thermal conductivity due to vapour migration. In addition, a computer simulation of a ground source heat pump system was developed, where the experimental data was used to investigate the impact of water content and soil texture variation on the GSHP performance. It was observed that the GSHP energy consumption varied more prominently when the soil wetness varied from dryness to full saturation and less significantly when the soil type varied from coarse to finer texture.

scholarly journals Experimental And Numerical Invetigation Of Dependence Of Soil Thermal Conductivity On Temperature Water Content, And Soil Texture

2021 ◽  
Author(s):  
Behnam Jowkar-Baniani
Keyword(s):  
Thermal Conductivity ◽  
Water Content ◽  
Soil Texture ◽  
Conductivity Data ◽  
Soil Thermal Conductivity ◽  
Pump System ◽  
Heat Pump System ◽  
Thermal Conductivity Data ◽  
Apparent Thermal Conductivity ◽  
The Impact

Comprehensive set of thermal conductivity data for a loam soil was generated, for temperature variations from 5ºC to 92ºC and water content variations from dry to saturation, and compared to two other soil textures. The results exhibited similar characteristics as those of the other textures, where a significant change in soil thermal conductivity was. Using the thermal conductivity data sets, a model representing heat and mass transfer in soil was used to study the apparent thermal conductivity due to vapour migration. In addition, a computer simulation of a ground source heat pump system was developed, where the experimental data was used to investigate the impact of water content and soil texture variation on the GSHP performance. It was observed that the GSHP energy consumption varied more prominently when the soil wetness varied from dryness to full saturation and less significantly when the soil type varied from coarse to finer texture.

scholarly journals Assessment of Soil Thermal Conductivity Based on BPNN Optimized by Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chenyang Liu ◽  
Xinmin Hu ◽  
Ren Yao ◽  
Yalu Han ◽  
Yong Wang ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Genetic Algorithm ◽  
Moisture Content ◽  
Control Variable ◽  
Thermal Engineering ◽  
Soil Thermal Conductivity ◽  
Undisturbed Soil ◽  
Prediction Ability ◽  
Natural Density ◽  
The Impact

Thermal conductivity is a critical parameter playing an important role in the heat transfer process in thermal engineering and enormous other engineering fields. Thus, the accurate acquisition of thermal conductivity has significant meaning for thermal engineering. However, compared to density test, moisture content test, and other physical property tests, the thermal conductivity is hard and expensive to acquire. Apparently, it has great meaning to accurately predict conductivity around a site through easily accessible parameters. In this paper, 40 samples are taken from 37 experimental points in Changchun, China, and the BPNN optimized by genetic algorithm (GA-BPNN) is used to evaluate the thermal conductivity by moisture content, porosity, and natural density of undisturbed soil. The result is compared by two widely used empirical methods and BPNN method and shows that the GA-BPNN has better prediction ability for soil thermal conductivity. The impact weight is obtained through mean impact value (MIV), where the natural density, moisture content, and porosity are 30.98%, 55.57%, and 13.45%, respectively. Due to high complexity of different parameter on thermal conductivity, some remolded soil specimens are taken to study the influence of individual factors on thermal conductivity. The correlations between moisture content and porosity with thermal conductivity are studied through control variable method. The result demonstrates that the impact weight of moisture content and porosity can be explained by remolded soil experiment to some extent.

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.

scholarly journals Numerical simulation of variable thermal conductivity on 3D flow of nanofluid over a stretching sheet

2020 ◽  
Vol 9 (1) ◽  
pp. 233-243 ◽  
Author(s):  
Nainaru Tarakaramu ◽  
P.V. Satya Narayana ◽  
Bhumarapu Venkateswarlu
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Stretching Sheet ◽  
Three Dimensional ◽  
Variable Thermal Conductivity ◽  
Normal Velocity ◽  
Transfer Coefficients ◽  
Similarity Transformations ◽  
Frictional Coefficients ◽  
The Impact

AbstractThe present investigation deals with the steady three-dimensional flow and heat transfer of nanofluids due to stretching sheet in the presence of magnetic field and heat source. Three types of water based nanoparticles namely, copper (Cu), aluminium oxide (Al2O3), and titanium dioxide (TiO2) are considered in this study. The temperature dependent variable thermal conductivity and thermal radiation has been introduced in the energy equation. Using suitable similarity transformations the dimensional non-linear expressions are converted into dimensionless system and are then solved numerically by Runge-Kutta-Fehlberg scheme along with well-known shooting technique. The impact of various flow parameters on axial and transverse velocities, temperature, surface frictional coefficients and rate of heat transfer coefficients are visualized both in qualitative and quantitative manners in the vicinity of stretching sheet. The results reviled that the temperature and velocity of the fluid rise with increasing values of variable thermal conductivity parameter. Also, the temperature and normal velocity of the fluid in case of Cu-water nanoparticles is more than that of Al2O3- water nanofluid. On the other hand, the axial velocity of the fluid in case of Al2O3- water nanofluid is more than that of TiO2nanoparticles. In addition, the current outcomes are matched with the previously published consequences and initiate to be a good contract as a limiting sense.

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.

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