Modelling Thermal Conduction in Nanoparticle Aggregates in the Presence of Surfactants

Abstract

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.

Modelling Thermal Conduction in Polydispersed and Sintered Nanoparticle Aggregates

“…In order to study the effect of polydispersity on thermal conductivity, the work developed in [ 38 ] has been extended to include polydispersed particles. This technique offered fast convergence of the algorithm for the representation of agglomerated systems with predetermined properties.…”

“…A new particle stochastically appears on the surface of the particle, and the process is repeated, with the restriction of no overlapping between any pair of particles. During the process, certain restrictions are imposed as described in [ 38 ], aiming at the convergence of the fractal dimension to the target value or range of values. It has been shown that the desired fractal dimension can be achieved with only a few particles.…”

“…The whole algorithm is repeated as many times as needed to satisfy the desired number of aggregates in the working domain. Further analytical descriptions of the method are presented in [ 38 ].…”

“…The present work examines the effects of polydispersity and sintering of particles on the effective thermal conductivity of nanofluids that contain particle aggregates. To this end, the method developed by the authors [ 38 ] for reconstructing particle aggregates is extended to include particle overlapping due to sintering as well as non-uniform particle size as a realistic outcome of nanofluid preparation. Among the merits of this method is the algorithmically rapid reconstruction of agglomerated systems with predetermined properties, namely, the fractal dimension and the average number of particles in the aggregates.…”

Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review

“…Many published numerical and experimental studies on nanofluids fabricated with particles of carbon-based materials were found in the literature, which show the continued growth of interest in such materials [ 35 , 51 , 52 , 53 , 54 ]. Figure 4 classifies these documents in terms of the number of available publications at the Scopus database for each type of carbon-based material used in nanofluids production.…”