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.

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

2012 ◽  
Vol 614-615 ◽  
pp. 688-694 ◽  
Author(s):  
Yi Wang ◽  
Guo Min Shen
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Water Table ◽  
Unsaturated Soil ◽  
Solid Phase ◽  
Saturated Soil ◽  
Soil Thermal Conductivity ◽  
Saturation Degree ◽  
Soil Thermal Properties ◽  
Soil Solid Phase

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.

scholarly journals Effects of Soil Moisture on the Responses of Soil Temperatures to Climate Change in Cold Regions*

2013 ◽  
Vol 26 (10) ◽  
pp. 3139-3158 ◽  
Author(s):  
Zachary M. Subin ◽  
Charles D. Koven ◽  
William J. Riley ◽  
Margaret S. Torn ◽  
David M. Lawrence ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Active Layer ◽  
Pore Space ◽  
Heat Of Fusion ◽  
Soil Thermal Conductivity ◽  
Temperature Changes ◽  
Community Land Model ◽  
Permafrost Soils ◽  
Soil Temperatures

Abstract At high latitudes, changes in soil moisture could alter soil temperatures independently of air temperature changes by interacting with the snow thermal rectifier. The authors investigated this mechanism with model experiments in the Community Land Model 4 (CLM4) with prescribed atmospheric forcing and vegetation state. Under equilibrium historical conditions, increasing CO2 concentrations experienced by plants from 285 to 857 ppm caused local increases in soil water-filled pore space of 0.1–0.2 in some regions throughout the globe. In permafrost regions that experienced this moistening, vertical- and annual- mean soil temperatures increased by up to 3°C (0.27°C averaged over all permafrost areas). A similar pattern of moistening and consequent warming occurred in simulations with prescribed June–September (JJAS) rainfall increases of 25% over historical values, a level of increase commensurate with projected future rainfall increases. There was a strong sensitivity of the moistening responses to the baseline hydrological state. Experiments with perturbed physics confirmed that the simulated warming in permafrost soils was caused by increases in the soil latent heat of fusion per unit volume and in the soil thermal conductivity due to the increased moisture. In transient Representative Concentration Pathway 8.5 (RCP8.5) scenario experiments, soil warming due to increased CO2 or JJAS rainfall was smaller in magnitude and spatial extent than in the equilibrium experiments. Active-layer deepening associated with soil moisture changes occurred over less than 8% of the current permafrost area because increased heat of fusion and soil thermal conductivity had compensating effects on active-layer depth. Ongoing modeling challenges make these results tentative.

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 Empirical and theoretical models for prediction of soil thermal conductivity: a review and critical assessment

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Adrian Różański ◽  
Natalia Kaczmarek
Keyword(s):  
Thermal Conductivity ◽  
Theoretical Models ◽  
Empirical Models ◽  
Critical Assessment ◽  
Point Of View ◽  
Soil Thermal Conductivity ◽  
Soil Medium ◽  
Fine Grained ◽  
Advantages And Disadvantages ◽  
Water Contents

AbstractThe paper discusses existing models used to estimate the thermal conductivity of the soil medium. The considerations are divided into three general sections. In the first section of the paper, we focus on the presentation of empirical models. Here, in the case of Johansen method, different relations for Kersten number are also presented. In the next part, theoretical models are considered. In the following part, selected models were used to predict measured thermal conductivities of coarse- and fine-grained soils, at different water contents. Based on these predictions as well as on the authors’ experience, a critical assessment of the existing models is provided. The remarks as well as advantages and disadvantages of those models are summarized in a tabular form. The latter is important from a practical point of view; based on the table content, one can simply choose a model that is suitable for the particular problem.

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.

PREDICTING THE EFFECT OF TEMPERATURE ON SOIL THERMAL CONDUCTIVITY

Soil Science ◽  
1994 ◽  
Vol 158 (5) ◽  
pp. 307-313 ◽  
Author(s):  
G. S. CAMPBELL ◽  
J. D. JUNGBAUER ◽  
W. R. BIDLAKE ◽  
R. D. HUNGERFORD
Keyword(s):  
Thermal Conductivity ◽  
Effect Of Temperature ◽  
Soil Thermal Conductivity

Optimization of thermal conductivity in composites loaded with the solid-solid phase-change materials

2018 ◽  
Vol 25 (6) ◽  
pp. 1157-1165
Author(s):  
Taoufik Mnasri ◽  
Adel Abbessi ◽  
Rached Ben Younes ◽  
Atef Mazioud
Keyword(s):  
Thermal Conductivity ◽  
Phase Change ◽  
Phase Change Materials ◽  
Solid Phase ◽  
Spherical Inclusions ◽  
First Case ◽  
The Matrix ◽  
Composite Conductivity ◽  
First Time

AbstractThis work focuses on identifying the thermal conductivity of composites loaded with phase-change materials (PCMs). Three configurations are studied: (1) the PCMs are divided into identical spherical inclusions arranged in one plane, (2) the PCMs are inserted into the matrix as a plate on the level of the same plane of arrangement, and (3) the PCMs are divided into identical spherical inclusions arranged periodically in the whole matrix. The percentage PCM/matrix is fixed for all cases. A comparison among the various situations is made for the first time, thus providing a new idea on how to insert PCMs into composite matrices. The results show that the composite conductivity is the most important consideration in the first case, precisely when the arrangement plane is parallel with the flux and diagonal to the entry face. In the present work, we are interested in exploring the solid-solid PCMs. The PCM polyurethane and a wood matrix are particularly studied.

Soil thermal conductivity prediction for district heating pre-insulated pipeline in operation

Energy ◽  
2012 ◽  
Vol 44 (1) ◽  
pp. 197-210 ◽  
Author(s):  
Matjaz Perpar ◽  
Zlatko Rek ◽  
Suvad Bajric ◽  
Iztok Zun
Keyword(s):  
Thermal Conductivity ◽  
District Heating ◽  
Soil Thermal Conductivity
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