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.
Many theoretical and experimental studies have shown that the addition of nanoparticles into conventional fluids may generate nanofluids with significantly improved heat transfer properties. In the present work, the effect of nanoparticle aggregation on the thermal conductivity of nanofluids is studied, considering also the effect of surfactants that are typically added to stabilise the nanofluid. A method for simulating aggregate formation is developed here that allows tailoring of the fractal dimension and the number density of the nanoparticles to desired values. The method is shown to be computationally simple and fast. Data that are extracted from electron microscope images are compared with simulation results regarding surface porosity and the autocorrelation function. The surfactants are modelled as a layer around the particles, and the effective thermal conductivity is calculated with a meshless numerical technique. Significant increase in conductivity is observed for small values of the fractal dimension and for large number density of particles in the aggregate. The simulations are in good agreement with experimental results. It is also concluded that prediction of the conductivity of such nanofluids requires the knowledge of the type and the amount of the surfactant added.
a b s t r a c tA numerical solution of the transient heat conduction problem with spatiotemporally variable conductivity in 2D space is obtained using the Meshless Local Petrov-Galerkin (MLPG) method. The approximation of the field variables is performed using Moving Least Squares (MLS) interpolation. The accuracy and the efficiency of the MLPG schemes are investigated through variation of (i) the domain resolution, (ii) the order of the basis functions, (iii) the shape of the integration site around each node, (iv) the conductivity range, and (v) the volumetric heat capacity range. Steady-state boundary conditions of the essential type are assumed. The results are compared with those calculated by a typical Finite Element method. Specific rectangular-type integration sites are introduced during both steady-state and transient MLPG integration, in order to provide complete surface coverage of the domain without overlapping, and the accuracy of the method is demonstrated in all cases studied. Computational efficiency is also investigated with this MLPG method and found to be slower than FE methods during construction stage, but it clearly surpasses that of FEM approaches during the solution stage on a wide parameter range.
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