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 A Simple Thermal Conductivity Model for Unsaturated Geomaterials Accounting for Degree of Saturation and Dry Density

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Agostino Walter Bruno ◽  
Doaa Alamoudi
Keyword(s):  
Thermal Conductivity ◽  
Coarse Sand ◽  
Degree Of Saturation ◽  
Dry Density ◽  
Conductivity Model ◽  
Thermal Conductivity Model ◽  
Dry Conditions ◽  
Density Factor ◽  
Materials Used ◽  
Different Levels

AbstractThis paper proposes a simple thermal conductivity model for geomaterials accounting for the combined effect of both degrees of saturation and dry density. The model only requires the determination of the thermal conductivity under dry conditions (i.e., at a degree of saturation equal to zero) and as little as two additional measurements of thermal conductivity performed at different levels of degree of saturation and dry density. The model is a function of only two fitting parameters, namely the moisture factor $${m}_{f}$$ m f and the density factor $${m}_{d}$$ m d . Despite its simplicity, the model can correctly predict the thermal conductivity of geomaterials and this has been validated against five sets of experimental data obtained on a very broad range of materials ranging from fine (e.g., bentonite) to coarser soils (e.g., a mix of gravel, coarse sand and silt) tested at different levels of degree of saturation and dry density. The paper also shows that the model can be applied to different engineering contexts such as (a) the thermal behaviour of earth materials used for building construction, (b) the thermal performance of bentonites employed for the storage of nuclear waste and (c) the estimation of the heat exchange in shallow geothermal reservoirs. Finally, the proposed model can be easily implemented in a finite element code to perform numerical simulations to study the heat transfer in unsaturated geomaterials.

Indirect methods to assess the solid particle thermal conductivity of Quebec marine clays

2007 ◽  
Vol 44 (9) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jean Côté ◽  
Jean-Marie Konrad
Keyword(s):  
Thermal Conductivity ◽  
Geometric Mean ◽  
Liquid Limit ◽  
Solid Particles ◽  
Particle Fraction ◽  
Empirical Relationships ◽  
Indirect Methods ◽  
Clay Particles ◽  
The Mean ◽  
Marine Clays

Thermal and frost action analyses in soils require the knowledge of the thermal conductivity of soil solid particles. This parameter was obtained using reverse modeling applied to thermal conductivity data of Quebec marine clays. Values ranged from 2.2 to 3.2 W/mK mostly due to variation of the quartz fraction. The mean thermal conductivity of forming minerals other than quartz was equal to 2.15 W/mK. A modified geometric mean model was thus proposed to estimate the thermal conductivity of clay solid particles based on the thermal conductivity of quartz and the mean thermal conductivity of the other minerals. Several data for soils in the literature were also analyzed to confirm the experimental results of this study and to further clarify the quartz fraction influence on the thermal conductivity of clay particles. Finally, analyses of basic geotechnical data from the literature helped establish empirical relationships for the estimation of the quartz fraction of a soil as a function of either the clay-size particle fraction or the liquid limit.

Freezing-induced water migration in compacted base-course materials

2008 ◽  
Vol 45 (7) ◽  
pp. 895-909 ◽  
Author(s):  
J.-M. Konrad
Keyword(s):  
Water Source ◽  
Degree Of Saturation ◽  
Coarse Grained ◽  
Frost Heave ◽  
Freezing Process ◽  
Course Materials ◽  
Base Course Material ◽  
Course Material ◽  
Base Course ◽  
External Water

Ramped-freezing tests were conducted on three base-course materials with fines contents of less than 7% and compacted at different initial states but always at degrees of saturation near or well below 60%. Three different quarries were studied. The natural fines from crushed gneiss with biotite, limestone, and monzonite were all frost susceptible. Frost heave was relatively small, but significant water intake occurred in all samples during freezing with access to an external water source, regardless of initial saturation level. The frost susceptibility of coarse-grained soils cannot be solely evaluated with respect to frost heave but needs also to consider the amount of water drawn to the freezing front during the freezing process and the consequences of this water during thaw. The normalized heave of the base-course layer of pavements is a practical and efficient indicator of the frost susceptibility of the base-course aggregates. If it is larger than 1%, the base-course material can be considered as frost susceptible leading to a significant increase in the degree of saturation once frozen. Current base-course material specifications based solely on grain-size distribution are not adequate to differentiate materials that are nonfrost susceptible from those that are frost susceptible. Hence, an additional criterion based on the fines frost susceptibility should be considered.

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.

Pool Boiling Characteristics of Metallic Nanofluids

2011 ◽  
Vol 133 (11) ◽  
Author(s):  
K. Hari Krishna ◽  
Harish Ganapathy ◽  
G. Sateesh ◽  
Sarit K. Das
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Boiling Heat Transfer ◽  
Volume Fraction ◽  
Pool Boiling ◽  
Heat Fluxes ◽  
Solid Particles ◽  
Heater Surface ◽  
Heat Transfer Characteristics ◽  
Transfer Characteristics

Nanofluids, solid-liquid suspensions with solid particles of size of the order of few nanometers, have created interest in many researchers because of their enhancement in thermal conductivity and convective heat transfer characteristics. Many studies have been done on the pool boiling characteristics of nanofluids, most of which have been with nanofluids containing oxide nanoparticles owing to the ease in their preparation. Deterioration in boiling heat transfer was observed in some studies. Metallic nanofluids having metal nanoparticles, which are known for their good heat transfer characteristics in bulk regime, reported drastic enhancement in thermal conductivity. The present paper investigates into the pool boiling characteristics of metallic nanofluids, in particular of Cu-H2O nanofluids, on flat copper heater surface. The results indicate that at comparatively low heat fluxes, there is deterioration in boiling heat transfer with very low particle volume fraction of 0.01%, and it increases with volume fraction and shows enhancement with 0.1%. However, the behavior is the other way around at high heat fluxes. The enhancement at low heat fluxes is due to the fact that the effect of formation of thin sorption layer of nanoparticles on heater surface, which causes deterioration by trapping the nucleation sites, is overshadowed by the increase in microlayer evaporation, which is due to enhancement in thermal conductivity. Same trend has been observed with variation in the surface roughness of the heater as well.

The Quantification of Internal Noise Levels and Wall Pressure Spectra in Industrial Gas Pipelines

1991 ◽  
Author(s):  
M. P. Norton ◽  
A. Pruiti
Keyword(s):  
Prediction Models ◽  
Transmission Loss ◽  
Pressure Fluctuations ◽  
Internal Noise ◽  
Wall Pressure ◽  
Gas Pipelines ◽  
Noise Levels ◽  
Empirical Prediction ◽  
Wall Pressure Fluctuations ◽  
Semi Empirical

Abstract This paper addresses the issue of quantifying the internal noise levels/wall pressure fluctuations in industrial gas pipelines. This quantification of internal noise levels/wall pressure fluctuations allows for external noise radiation from pipelines to be specified in absolute levels via appropriate noise prediction models. Semi-empirical prediction models based upon (i) estimated vibration levels and radiation ratios, (ii) semi-empirical transmission loss models, and (iii) statistical energy analysis models have already been reported on by Norton and Pruiti 1,3 and are not reported on here.

Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach

2021 ◽  
Vol 106 ◽  
pp. 109-115
Author(s):  
L.B. Abhang ◽  
M. Hameedullah
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Prediction Models ◽  
Tool Geometry ◽  
Modeling And Analysis ◽  
Empirical Prediction ◽  
Lathe Machine ◽  
Carbide Inserts ◽  
Computing Approach

The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and tool geometry on the surface finish values. Research showed that soft computing fuzzy logic model developed produces smaller error and has satisfactory results as compared to response regression model during machining.

scholarly journals Thermal conductivity of sandstones from Biot’s coefficient

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. D173-D185 ◽  
Author(s):  
Tobias Orlander ◽  
Eirini Adamopoulou ◽  
Janus Jerver Asmussen ◽  
Adam Andrzej Marczyński ◽  
Harald Milsch ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Flow ◽  
Cross Section ◽  
Geometric Mean ◽  
Well Log ◽  
Biot’S Coefficient ◽  
Model Predictions ◽  
The Cross ◽  
Rock Texture

Thermal conductivity of rocks is typically measured on core samples and cannot be directly measured from logs. We have developed a method to estimate thermal conductivity from logging data, where the key parameter is rock elasticity. This will be relevant for the subsurface industry. Present models for thermal conductivity are typically based primarily on porosity and are limited by inherent constraints and inadequate characterization of the rock texture and can therefore be inaccurate. Provided known or estimated mineralogy, we have developed a theoretical model for prediction of thermal conductivity with application to sandstones. Input parameters are derived from standard logging campaigns through conventional log interpretation. The model is formulated from a simplified rock cube enclosed in a unit volume, where a 1D heat flow passes through constituents in three parallel heat paths: solid, fluid, and solid-fluid in series. The cross section of each path perpendicular to the heat flow represents the rock texture: (1) The cross section with heat transfer through the solid alone is limited by grain contacts, and it is equal to the area governing the material stiffness and quantified through Biot’s coefficient. (2) The cross section with heat transfer through the fluid alone is equal to the area governing fluid flow in the same direction and quantified by a factor analogous to Kozeny’s factor for permeability. (3) The residual cross section involves the residual constituents in the solid-fluid heat path. By using laboratory data for outcrop sandstones and well-log data from a Triassic sandstone formation in Denmark, we compared measured thermal conductivity with our model predictions as well as to the more conventional porosity-based geometric mean. For outcrop material, we find good agreement with model predictions from our work and with the geometric mean, whereas when using well-log data, our model predictions indicate better agreement.

Sign in / Sign up

Export Citation Format

Share Document