Determination of Thermal Conductivity of Soils: A Need for Computing Heat Loss Through Buried Submarine Pipelines

1982 ◽  
Vol 22 (04) ◽  
pp. 558-562 ◽  
Author(s):  
P.C. Rawat ◽  
S.L. Agarwal

Abstract An important parameter required for computing heat loss through buried submarine pipelines transporting crude oil is the thermal conductivity of soils. This paper describes an apparatus designed for determination of the thermal conductivity of soils at the desired moisture/ density condition in the laboratory under steady-state conditions. Experimental results on the three soils studied show that thermal conductivity increases as dry density increases at a constant moisture content and that it increases as water content increases at constant dry density. These results confirm the trends isolated earlier by Kersten. The experimental results are compared with the available empirical relationships. Kersten's relation is observed to predict the thermal conductivity of these soils reasonably. The predictions from Makowski and Mochlinski's relation (quoted by Szilas) are not good but improve if the sum of silt and clay fractions is treated as a clay fraction in the computation. Introduction Submarine pipelines are used extensively for transporting crude oil from offshore to other pipelines offshore or onshore. These pipelines usually are steel pipes covered with a coating of concrete. They often are buried some depth below the mudline. The rheological properties of different crude oils vary, and their viscosities increase with a decrease in temperature. Below some temperature, the liquid oil tends to gel. Therefore, for efficient transportation, the crude must be at a relatively high temperature so that it has a low viscosity. The temperature of the soil/water system surrounding a submarine pipeline is usually lower than that of oil. This temperature difference induces heat to flow from the oil to the environment, and the temperature of the oil decreases as it travels along the length of the pipeline. One must ensure that this temperature reduction does not exceed desirable limits dictated by the rheological properties of oil and by the imperatives of efficient economic properties of oil and by the imperatives of efficient economic transportation. Thus the analytical problem is to predict the temperature of crude in the pipeline some distance away from the input station. To do so, knowledge of the overall heat transfer coefficient for the pipeline is required, for which, in turn, it is necessary to know the thermal conductivities of the oil, the pipeline materials and its coating, and the soil. This paper presents thermal conductivities of soils determined in the laboratory under steady-state conditions and also presents a comparison of the test results of three soils with values determined from existing empirical relationships. Literature Review Heat moves spontaneously from higher to lower temperatures. In a completely dry porous body, transmission of heat can take place not only by conduction through the solid framework of the body and the air in the pores but also by convection and radiation between the walls of a pore and by macro- and microdistillation. In soils, however, it can be ascribed essentially to conduction, a molecular phenomenon that can be expressed in terms of experimentally determined coefficients of conductivity or resistivity, although these actually may include microdistillation and other mechanisms. SPEJ p. 558

Author(s):  
Milivoje M. Kostic ◽  
Casey J. Walleck

A steady-state, parallel-plate thermal conductivity (PPTC) apparatus has been developed and used for comparative measurements of complex POLY-nanofluids, in order to compare results with the corresponding measurements using the transient, hotwire thermal conductivity (HWTC) apparatus. The related measurements in the literature, mostly with HWTC method, have been inconsistent and with measured thermal conductivities far beyond prediction using the well-known mixture theory. The objective was to check out if existing and well-established HWTC method might have some unknown issues while measuring TC of complex nano-mixture suspensions, like electro-magnetic phenomena, undetectable hot-wire vibrations, and others. These initial and limited measurements have shown considerable difference between the two methods, where the TC enhancements measured with PPTC apparatus were about three times smaller than with HWTC apparatus, the former data being much closer to the mixture theory prediction. However, the influence of measurement method is not conclusive since it has been observed that the complex nano-mixture suspensions were very unstable during the lengthy steady-state measurements as compared to rather quick transient HWTC method. The nanofluid suspension instability might be the main reason for very inconsistent results in the literature. It is necessary to expend investigation with more stable nano-mixture suspensions.


The analysis of the dynamic theory of gases has indicated an interesting relation between the viscosity η , the thermal conductivity K, and the specific heat at constant volume C c of a gas. This relation is represented by the expression K = f . C c . η , in which the factor f depends upon the law of force operative in molecular collision, and is known if K, C c , and η can be determined experimentally. In view of its importance in this respect, and also from the fact that great accuracy and consistency of measurement are possible in modern determinations of the viscosity of gases, the importance of the development of a method by which the conductivity can be measured with the same order of accuracy demands increasing attention.


1979 ◽  
Author(s):  
P.C. Rawat ◽  
S.L. Agarwal ◽  
A.A. Malhotra ◽  
S.K. Gulhati ◽  
G.V. Rao

The interest in the determination of the thermal conductivities of oxygen and nitrogen lies partly in their relation to the thermal conductivity of air. The latter is the medium which practically every experimenter on gaseous thermal conduction has investigated, and has therefore become the standard substance in this field of research. Being a mixture chiefly of the gases oxygen and nitrogen, with the latter in the greater proportion, the value of its conductivity should lie between those of oxygen and nitrogen and should be nearer that of nitrogen than that of oxygen. The authors, in common with Weber and Todd, have verified this experimentally, the only observer finding these con­ductivities in a contrary order being Winkelman, who used a cooling thermometer method. The following is a table of the results hitherto obtained for the absolute thermal conductivities at 0° C. of oxygen and nitrogen, together with their authors’ results for air. The values marked with an asterisk have been deduced by applying the temperature coefficient, 0.0029 per 0° C., to results which were obtained at temperatures above 0° C. Weber has recently published a new result for the thermal conductivity of air, 0.0000574, which is about 1 per cent. higher than his old value. Assuming that, if his results for oxygen and nitrogen were revised, they would be increased in the same proportion, his new values for these gases would be—oxygen 0.0000583, and nitrogen 0.0000572.


2017 ◽  
Vol 64 (3) ◽  
pp. 169-180 ◽  
Author(s):  
Oluseun Adetola Sanuade ◽  
Rasheed Babatunde Adesina ◽  
Joel Olayide Amosun ◽  
Akindeji Opeyemi Fajana ◽  
Olayiwola Grace Olaseeni

Abstract Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.


Sign in / Sign up

Export Citation Format

Share Document