On thermal characteristics of coarse grained and fine grained soils in ground heat exchanger applications

Tadza, Mohd Yuhyi Mohd; Karim, Asar Abdul; Abdullah, Amizatulhani; Mohamed, Danial; Yusof, Taib Mohd

doi:10.1063/1.5118085

Cited by 1 publication

(1 citation statement)

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“…As a main thermodynamic parameter of soil, thermal diffusivity reflects the synergistic effect of thermal conductivity and specific heat capacity in the heat transfer process [4,5]. Thermal diffusivity describes the instantaneous process of heat transport under certain temperature boundary conditions [6], which can be obtained by theoretical calculation and an experimental testing method.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil

et al. 2022

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Revealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content on thermal diffusivity are analyzed in this work. Then, the prediction model based on the empirical fitting formula and RBF neural network method for thermal diffusivity of frozen and unfrozen sandy soil is established, and the prediction accuracy of different prediction methods is compared. The results show that (1) thermal diffusivity of sandy soil is positively correlated with the particle size. With the increase of sand size, thermal diffusivity of sandy soil increases significantly. (2) Partial correlation among natural moisture content, dry density, and thermal diffusivity varies with different frozen and unfrozen conditions. (3) For unfrozen sandy soil, the binary RBF neural network prediction model is obviously better than that of the binary empirical fitting formula model. (4) The ternary prediction model has significantly higher prediction accuracy than that of the binary prediction model for frozen sandy soil, and the ternary RBF neural network model has the best prediction effect among the four methods.

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Section: Introductionmentioning

confidence: 99%