A new meshless numerical approach for studying heat conduction in particulate systems was developed that allows the efficient computation of the temperature distribution and the effective thermal conductivity in particle aggregates. The incorporation of the discretization-corrected particle strength exchange operator in meshless local Petrov–Galerkin calculations is suggested here, which was shown to perform better than previously tested trial functions, regarding the speed of convergence and accuracy. Moreover, an automated algorithm for node refinement was developed, which avoids the necessity for user intervention. This was quite important in the study of particle aggregates due to the appearance of multiple points of contact between particles. An alternative approach for interpolation is also presented, that increased the stability of the methods and reduced the computational cost. Test case models, commercial computational fluid dynamics software, and experimental data were used for validation. Heat transport in various aggregate morphologies was also studied using sophisticated aggregation models, in order to quantify the effect of aggregate fractal dimension on the nanofluid conductivity, targeting eventually the optimization of heat transfer applications. A trend of effective conductivity enhancement upon reduction of the fractal dimension of the aggregate was noted.
An Efficient Meshless Numerical Method for Heat Conduction Studies in Particle Aggregates
