Soft and hard computation methods for estimation of the effective thermal conductivity of sands

Abstract

Thermal properties of sand are of importance in numerous engineering and scientific applications ranging from energy storage and transportation infrastructures to underground construction. All these applications require knowledge of the effective thermal parameters for proper operation. The traditional approaches for determination of the effective thermal property, such as the thermal conductivity are based on very costly, tedious and time-consuming experiments. The recent developments in computer science have allowed the use of soft and hard computational methods to compute the effective thermal conductivity (ETC). Here, two computation methods are presented based on soft and hard computing approaches, namely, the deep neural network (DNN) and the thermal lattice element method (TLEM), respectively, to compute the ETC of sands with varying porosity and moisture content values. The developed models are verified and validated with a small data set reported in the literature. The computation results are compared with the experiments, and the numerical results are found to be within reasonable error bounds. The deep learning method offers fast and robust implementation and computation, even with a small data set due to its superior backpropagation algorithm. However, the TLEM based on micro and meso physical laws outperforms it at accuracy.

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

“…Although the models use the ANN methodology, the deep learning (DL) implementation is not employed. Both the networks, as mentioned earlier, are only limited to one or two hidden layers which limit the ANN's ability for forecasting the scenarios other than for which it is trained [15]. A prerequisite for the implementation of DL is the availability of large and varied data set, as shown in Fig.…”

“…Starting with only two hidden layers with four neurons each, we computed till three hidden layers with ten neurons each. The author has used a combination of only even neurons in each hidden layer based on his previous experiences [15]. The activation functions help us determine the learning rate.…”

“…The approach mainly used in civil engineering is the Artificial Neural Network (ANN) which is derived from the deep learning algorithm [12]. In the past researchers have used ANN to predict the thermal conductivity of soil from moisture content and dry density [13][14][15]. However, many different studies have pointed out the importance of the mineral composition of the soil and its effect on thermal conductivity [16].…”

Evolution of Temperature Field around Underground Power Cable for Static and Cyclic Heating

“…The effective thermal conductivity (ETC) of soil depends on the degree of saturation, density, temperature, mineralogical composition, particle size, shape, gradation, interparticle physical contact, properties of soil components, ions, salts, additives, hysteresis effect, etc. [48]. However, the dominant factors controlling the changes are the environmental factors, such as the degree of saturation, porosity and temperature.…”

The latest mathematical and empirical models to calculate the thermal conductivity of the soils

“…The input parameters in the input layer are clay content, silt content, sand content, thermal conductivity of solids, thermal conductivity of dried soils, thermal conductivity of saturated soils, quartz content and porosity. Rizvi et al [40] developed an artificial neural network (ANN) model based on a fast learning (DL) algorithm. This model is applicable to the calculation of the effective thermal conductivity of unsaturated sandy soils.…”