A fast and accurate implementation of tunable algorithms used for generation of fractal-like aggregate models

In many branches of science experiments are expensive, require specialist equipment or are very time consuming. Studying the light scattering phenomenon by fractal aggregates can serve as an example. Light scattering simulations can overcome these problems and provide us with theoretical, additional data which complete our study. For this reason a fractal-like aggregate model as well as fast aggregation codes are needed. Until now various computer models, that try to mimic the physics behind this phenomenon, have been developed. However, their implementations are mostly based on a trial-and-error procedure. Such approach is very time consuming and the morphological parameters of resulting aggregates are not exact because the postconditions (e.g. the position error) cannot be very strict. In this paper we present a very fast and accurate implementation of a tunable aggregation algorithm based on the work of Filippov et al. (2000). Randomization is reduced to its necessary minimum (our technique can be more than 1000 times faster than standard algorithms) and the position of a new particle, or a cluster, is calculated with algebraic methods. Therefore, the postconditions can be extremely strict and the resulting errors negligible (e.g. the position error can be recognized as non-existent). In our paper two different methods, which are based on the particle–cluster (PC) and the cluster–cluster (CC) aggregation processes, are presented.