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
Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5712
Author(s):  
Matjaž Perpar ◽  
Zlatko Rek

We carried out several numerical experiments to analyze how different boundary conditions affect the ability to detect small pipeline leaks. Our method is based on determining the soil temperature gradient above a buried district heating channel. The equivalent thermal conductivity of a wet insulation (λeq) value of 0.5 W/(m·K) was used to mimic a small water leakage. To evaluate the heat loss through the channel cross section, the heat conduction model was used for the pipe insulation, the concrete, and the soil, while the convection model was considered within the channel. The following effects were used to simulate different operating conditions: heat convection at the soil surface, leakage only from the supply or return pipe, soil height above the channel, soil thermal conductivity, and pipe diameter. With the exception of leakage only from the return pipe and low soil thermal conductivity 0.4 W/(m·K), the results showed a doubling of the soil temperature gradient when compared with the no-leakage case. This fact undoubtedly confirms the potential of the method, which is particularly suitable for leak detection in old pipelines that have priority for renovation. A key added value of this research is that the soil temperature gradient-based leak detection technique was found useful in most foreseeable DH operating situations.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  

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.


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

2012 ◽  
Vol 614-615 ◽  
pp. 688-694 ◽  
Author(s):  
Yi Wang ◽  
Guo Min Shen

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.


Author(s):  
Babafemi Olugunwa ◽  
Julia Race ◽  
Tahsin Tezdogan

Abstract Pipeline heat transfer modelling of buried pipelines is integral to the design and operation of onshore pipelines to aid the reduction of flow assurance challenges such as carbon dioxide (CO2) gas hydrate formation during pipeline transportation of dense phase CO2 in carbon capture and storage (CCS) applications. In CO2 pipelines for CCS, there are still challenges and gaps in knowledge in the pipeline transportation of supercritical CO2 due to its unique thermophysical properties as a single, dense phase liquid above its critical point. Although the design and operation of pipelines for bulk fluid transport is well established, the design stage is incomplete without the heat transfer calculations as part of the steady state hydraulic and flow assurance design stages. This paper investigates the steady state heat transfer in a buried onshore dense phase CO2 pipelines analytically using the conduction shape factor and thermal resistance method to evaluate for the heat loss from an uninsulated pipeline. A parametric study that critically analyses the effect of variation in pipeline burial depth and soil thermal conductivity on the heat transfer rate, soil thermal resistance and the overall heat transfer coefficient (OHTC) is investigated. This is done using a one-dimensional heat conduction model at constant temperature of the dense phase CO2 fluid. The results presented show that the influence of soil thermal conductivity and pipeline burial depth on the rate of heat transfer, soil thermal resistance and OHTC is dependent on the average constant ambient temperature in buried dense phase CO2 onshore pipelines. Modelling results show that there are significant effects of the ambient natural convection on the soil temperature distribution which creates a thermal influence region in the soil along the pipeline that cannot be ignored in the steady state modelling and as such should be modelled as a conjugate heat transfer problem during pipeline design.


2014 ◽  
Vol 53 (8) ◽  
pp. 1976-1995 ◽  
Author(s):  
Jeffrey D. Massey ◽  
W. James Steenburgh ◽  
Sebastian W. Hoch ◽  
Jason C. Knievel

AbstractWeather Research and Forecasting Model forecasts over the Great Salt Lake Desert erroneously underpredict nocturnal cooling over the sparsely vegetated silt loam soil area of Dugway Proving Ground in northern Utah, with a mean positive bias error in temperature at 2 m AGL of 3.4°C in the early morning [1200 UTC (0500 LST)]. Positive early-morning bias errors also exist in nearby sandy loam soil areas. These biases are related to the improper initialization of soil moisture and parameterization of soil thermal conductivity in silt loam and sandy loam soils. Forecasts of 2-m temperature can be improved by initializing with observed soil moisture and by replacing Johansen's 1975 parameterization of soil thermal conductivity in the Noah land surface model with that proposed by McCumber and Pielke in 1981 for silt loam and sandy loam soils. Case studies illustrate that this change can dramatically reduce nighttime warm biases in 2-m temperature over silt loam and sandy loam soils, with the greatest improvement during periods of low soil moisture. Predicted ground heat flux, soil thermal conductivity, near-surface radiative fluxes, and low-level thermal profiles also more closely match observations. Similar results are anticipated in other dryland regions with analogous soil types, sparse vegetation, and low soil moisture.


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