Development of correlations for soil thermal conductivity

Becker, Bryan R.; Misra, Anil; Fricke, Brian

doi:10.1016/0735-1933(92)90064-o

Cited by 72 publications

(32 citation statements)

References 8 publications

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“…Past studies focused on the effect of porosity on thermal conductivity for specific field locations or unimodal soils rather than soil mixtures. Soil type also affects l. Soils with relatively flat grain surfaces, such as silts and clays, have more contact points between grains and higher l than do more rounded particles like sands and gravels, which have smaller contact area and lower l (Becker et al, 1992). Grain size also affects l where coarse-grained materials often maintain a higher l than fine-grained materials (Al Nakshabandi and Kohnke, 1965;Côté and Konrad, 2009).…”

mentioning

confidence: 99%

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

Wallen

Smits

Sakaki

et al. 2016

Soil Science Soc of Amer J

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A soil's grain-size distribution affects its physical and hydraulic properties; however, little is known about its effect on soil thermal properties. To better understand how grain-size distribution affects soil thermal properties, specifically the effective thermal conductivity, a set of laboratory experiments was performed using binary mixtures of two uniform sands tightly packed with seven different mixing fractions over the full range of saturation. For each binary mixture, the effective thermal conductivity, l, capillary pressure, h c , and volumetric water content, q, were measured. Results demonstrated that the l-q relationship exhibited distinct characteristics based on the percentage of fine-and coarse-grained sands. We further compared measured l-q properties with independent estimates from two semi-empirical models (Campbell Model and Lu and Dong Model) to evaluate the models' applicability in relation to physically based parameters associated with changes in soil mixing (e.g., porosity and grain size). Both models were able to fit experimental data but to varying degrees based on the number of physically based parameters used. In general, model improvements are needed to capture the l-q relationship solely on physically based parameters.Abbreviations: SWRC, soil water retention curve. U nderstanding soil thermal properties is important to properly determine the impact of heat transfer on the distribution, circulation, and evaporation of water in soil. Heat transfer within soil influences micrometeorological phenomena (Hanks et al., 1967), engineering efforts such as cooling electronic devices with heat pipes and insulating buildings (Bussing and Bart, 1997;Sakaguchi et al., 2009;Moradi et al., 2015), agricultural production (Al Nakshabandi and Kohnke, 1965;Usowicz et al., 1996;Lipiec et al., 2007), and landmine detection efforts that rely on thermal contrast (e.g., Garcia-Padron et al., 2002;Martínez et al., 2004). Previous studies provide insight into the effect of soil moisture, temperature, and soil physical characteristics on soil thermal properties, specifically the effective thermal conductivity, l (W m -1 K -1 ; e.g., De Vries, 1963;Johansen, 1975;Hopmans and Dane, 1986;Ochsner et al., 2001;Tarnawski and Gori, 2002). However, to our knowledge, none of the studies to date has investigated the effect of mixing differently sized particles on controlling the l behavior under varying soil water contents (q, cm 3 cm -3 ). Knowledge of the l-q relationship for differently sized soil fractions is particularly important in many engineering applications such as soil borehole thermal energy storage (SBTES), landfill covers, and engineered material stabilization.Previous research provides strong evidence that l is affected by a wide range of parameters including q (e.g., Philip and de Vries, 1957;De Vries, 1963;Yadav and Saxena, 1977; Core Ideas• Thermal conductivity and water content relationship based upon mixtures are distinct.• Systematic change in particle fine fraction directly affects thermal cond...

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confidence: 99%

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

Wallen

Smits

Sakaki

et al. 2016

Soil Science Soc of Amer J

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“…(adapted after Becker et al, 1992;Becker and Fricke, 1997) Sundberg, 1988;Tarnawski and Leong, 2000) where ϕ q is the volume fraction of quartz, ϕ m is the volume fraction of other minerals, and ϕ s = ϕ q + ϕ m is the volume fraction of solids. Meanwhile coefficient C is highly correlated with the clay fraction, m c .…”

Section: Nomentioning

confidence: 98%

“…4 Becker et al (1992), Becker and Fricke (1997) Based on measured thermal conductivity data available in the literature, another empirical methodology was developed by Becker et al (1992). In this formula, a 1 , a 2 , a 3 and a 4 are coefficients dependent upon soil type in either the frozen or unfrozen state.…”

Section: Nomentioning

confidence: 99%

The sensitivity of ground surface temperature prediction to soil thermal properties Using the Simple Biosphere Model (SiB2)

Zhang

Gao

2012

Adv. Atmos. Sci.

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Using the Simple Biosphere Model (SiB2), soil thermal properties (STP) were examined in a Tibetan prairie during the monsoon period to investigate ground surface temperature prediction. We improved the SiB2 model by incorporating a revised force-restore method (FRM) to take the vertical heterogeneity of soil thermal diffusivity (k) into account. The results indicate that (1) the revised FRM alleviates daytime overestimation and nighttime underestimation in modeled ground surface temperature (Tg), and (2) its role in little rainfall events is significant because the vertical gradient of k increases with increasing surface evaporation. Since the original formula of thermal conductivity (λ) in the SiB2 greatly underestimates soil thermal conductivity, we compared five algorithms of λ involving soil moisture to investigate the cause of overestimation during the day and underestimation at night on the basis of the revised FRM. The results show that (1) the five algorithms significantly improve Tg prediction, especially in daytime, and (2) taking one of these five algorithms as an example, the simulated Tg values in the daytime are closer to the field measurements than those in the nighttime. The differences between modeled Tg and field measurements are mostly within the margin of error of ±2 K during 3 Citation: Zhang, X. H., Z. Q. Gao, and D. P. Wei, 2012: The sensitivity of ground surface temperature prediction to soil thermal properties using the simple biosphere model (SiB2).

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“…While in the early 1970s and 1980s the studies approached a experimental recognition of the thermo-physical behavior of the soil and underground [13], and thenceforth determined mathematical methods of predicting this behavior [14], in the late 1980s and 1990s accurate numerical models where developed and tested [14][15][16][17][18]. In more recent studies the researchers seek for optimizing performance of the systems, with solar assisted ground source heat pumps [19], more improved configurations [20][21][22], or testing more efficient sources like groundwater [22,23].…”

Section: Climate Conditions Depth and Soil Surface Temperaturementioning

confidence: 99%

Cooling and heating potential of underground soil according to depth and soil surface treatment in the Brazilian climatic regions

Alves

Schmid

2015

Energy and Buildings

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Development of correlations for soil thermal conductivity

Cited by 72 publications

References 8 publications

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

Thermal Conductivity of Binary Sand Mixtures Evaluated through Full Water Content Range

The sensitivity of ground surface temperature prediction to soil thermal properties Using the Simple Biosphere Model (SiB2)

Cooling and heating potential of underground soil according to depth and soil surface treatment in the Brazilian climatic regions

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