V. Cregan | Searcharxiv

arXiv2019

Does mathematics contribute to the nanofluid debate?

Recent experimental evidence has clearly demonstrated that nanofluids do not provide the greatly enhanced heat transfer predicted in the past. Despite seemingly conclusive proof there is still a great deal of current mathematical research asserting the opposite result. In this paper we scrutinise the mathematical work and demonstrate that the disagreement can be traced to a number of issues. These include the incorrect formulation of the governing equations; the use of parameter values orders of magnitude different to the true values (some requiring nanoparticle volume fractions greater than unity and nanoparticles smaller than atoms); model choices that are based on permitting a reduction using similarity variables as opposed to representing an actual physical situation; presentation of results using different scalings for each fluid.

physics.flu-dyn↗

arXiv2019

Nanocrystal growth via the precipitation method

A mathematical model to describe the growth of an arbitrarily large number of nanocrystals from solution is presented. First, the model for a single particle is developed. By non-dimensionalising the system we are able to determine the dominant terms and reduce it to the standard pseudo-steady approximation. The range of applicability and further reductions are discussed. An approximate analytical solution is also presented. The one particle model is then generalised to $N$ well dispersed particles. By setting $N=2$ we are able to investigate in detail the process of Ostwald ripening. The various models, the $N$ particle, single particle and the analytical solution are compared against experimental data, all showing excellent agreement. By allowing $N$ to increase we show that the single particle model may be considered as representing the average radius of a system with a large number of particles. Following a similar argument the $N=2$ model could describe an initially bimodal distribution. The mathematical solution clearly shows the effect of problem parameters on the growth process and, significantly, that there is a single controlling group. The model provides a simple way to understand nanocrystal growth and hence to guide and optimise the process.

cond-mat.mes-hall↗