Solution of population balance equations in applications with fine particles: Mathematical modeling and numerical schemes
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The accurate description and robust simulation, at relatively low cost, of global quantities (e.g. number density or volume fraction) as well as the size distribution of a population of fine particles in a carrier fluid is still a major challenge for many applications. For this purpose, two types of methods are investigated for solving the population balance equation with aggregation, continuous particle size change (growth and size reduction), and nucleation: the extended quadrature method of moments (EQMOM) based on the work of Yuan et al.and a hybrid method (TSM) between the sectional and moment methods, considering two moments per section based on the work of Laurent et al.. For both methods, the closure employs a continuous reconstruction of the number density function of the particles from its moments, thus allowing evaluation of all the unclosed terms in the moment equations, including the negative flux due to the disappearance of particles. Here, new robust and efficient algorithms are developed for this reconstruction step and two kinds of reconstruction are tested for each method. Moreover, robust and accurate numerical methods are developed, ensuring the realizability of the moments. The robustness is ensured with efficient and tractable algorithms despite the numerous couplings and various algebraic constraints thanks to a tailored overall strategy. EQMOM and TSM are compared to a sectional method for various simple but relevant test cases, showing their ability to describe accurately the fine-particle population with a much lower number of variables. These results demonstrate the efficiency of the modeling and numerical choices, and their potential for the simulation of real-world applications.
This article is published as Nguyen, Tan Trung, Frédérique Laurent, Rodney O. Fox, and Marc Massot. "Solution of population balance equations in applications with fine particles: mathematical modeling and numerical schemes." Journal of Computational Physics 325 (2016): 129-156. DOI: 10.1016/j.jcp.2016.08.017. Posted with permission.