FracVAL: An improved tunable algorithm of cluster-cluster aggregation for generation of fractal structures formed by polydisperse primary particles

Published: 21 February 2019| Version 1 | DOI: 10.17632/mgf8wdcsfb.1


In this study, the tunable algorithm of cluster-cluster aggregation developed by Filippov et al. (2000) for generating fractal aggregates formed by monodisperse spherical primary particles is extended to polydisperse primary particles. This new algorithm, termed FracVAL, is developed by using an innovative aggregation strategy. The algorithm is able to preserve the prescribed fractal dimension (D_f) and prefactor (k_f) for each aggregate, regardless of its size, with negligible error for lognormally distributed primary particles with the geometric standard deviation being as large as 3. In contrast, for polydisperse primary particles the direct use of Filippov et al. (2000) method, as is done by Skorupski et al. (2014), does not ensure the preservation of D_f and k_f for individual aggregates and it is necessary to generate a large number of aggregates to achieve the prescribed D_f and k_f on an ensemble basis. The performance of FracVAL is evaluated for aggregates consisting of 500 and 1000 monomers and for fractal dimension variation over the entire range of D_f between 1 and 3 and k_f between 0.1 and 2.7. Aggregates consisting of 500 monomers are generated on average in less than 2.4 min on a common laptop, illustrating the efficiency of the proposed algorithm.



Computational Physics