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JOURNALS // Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki // Archive

Zh. Vychisl. Mat. Mat. Fiz., 2022 Volume 62, Number 2, Pages 255–269 (Mi zvmmf11359)

Partial Differential Equations

Kinetic aggregation models leading to morphological memory of formed structures

S. Z. Adzhieva, V. V. Vedenyapinbc, I. V. Melikhova

a Faculty of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
b Federal Research Center Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047, Moscow, Russia
c RUDN University, 117198, Moscow, Russia

Abstract: Kinetic equations describing the evolution of dispersed particles with different properties (such as the size, velocity, center-of-mass coordinates, etc.) are discussed. The goal is to develop an a priori mathematical model and to determine the coefficients of the resulting equations from experimentally obtained distribution functions. Accordingly, the task is to derive valid (physicochemically justified) aggregation equations. The system of equations describing the evolution of the discrete distribution function of dispersed particles is used to obtain continuum equations of the Fokker–Planck or Einstein–Kolmogorov type or a diffusion approximation to the distribution function of aggregating particles differing in the level of aggregation and in the number of constituent molecules. Distribution functions approximating experimental data are considered, and they are used to determine the coefficients of a Fokker–Planck-type equation.

Key words: particle dispersion, particle distribution function with respect to properties, formation and growth of particles, aggregation and agglomeration, Smoluchowski system of equations, Becker–Döring equations, Fokker–Planck equation, diffusion approximation, morphological memory.

UDC: 517.958

Received: 20.06.2021
Revised: 27.07.2021
Accepted: 12.10.2021

DOI: 10.31857/S0044466922020028


 English version:
Computational Mathematics and Mathematical Physics, 2022, 62:2, 254–268

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© Steklov Math. Inst. of RAS, 2024