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.
Highlights
Thermal properties of sand, especially, the Effective Thermal Conductivity (ETC), is of importance in many engineering and scientific applications and investigations [1,2,3]
We propose two soft computational approaches based on Thermal Lattice Element Method (TLEM) and Deep Learning Network (DLN)
The soft computational approach is implemented with a neural network based on the deep learning and Adam optimiser pertaining to its minimal data set requirement for training and validation
Summary
Thermal properties of sand, especially, the Effective Thermal Conductivity (ETC), is of importance in many engineering and scientific applications and investigations [1,2,3]. The limitation of these methods is that they can only predict the scenarios for which they are trained [11] Another approach to developing the neural network is to put two or more hidden layers generating a deep web or intricately connected layers. The model was only implemented for the 2D scenario and required significant computational resources In another hybrid approach, the checkerboard analogy [34] and the analytical homogenization based on Mori-Tanaka scheme [35] was applied to compute the effective thermal conductivity of two-phase and threephase granular media, respectively. We propose two soft computational approaches based on Thermal Lattice Element Method (TLEM) and Deep Learning Network (DLN).
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