Numerical investigation of spatially nonhomogeneous acoustic agglomeration using sectional algorithm

Abstract

In the simulation of acoustic agglomeration, the conventional temporal model assumes spatial homogeneity in aerosol properties and sound field, which is often not the case in real applications. In this article, we investigated the effects of spatial nonhomogeneity of sound field on the acoustic agglomeration process through a one-dimensional spatial sectional model. The spatial sectional model is validated against existing experimental data and results indicate lower requirements on the number of sections and better accuracy. Two typical cases of spatial nonhomogeneous acoustic agglomeration are studied by the established model. The first case involves acoustic agglomeration in a standing wave field with spatial alternation of acoustic kernels from nodes to antinodes. The good agreement between the simulation and experiments demonstrates the predictive capability of the present spatial sectional model for the standing-conditioned agglomeration. The second case incorporates sound attenuation in the particulate medium into acoustic agglomeration. Results indicate that sound attenuation can influence acoustic agglomeration significantly, particularly at high frequencies, and neglecting the effects of sound attenuation can cause overprediction of agglomeration rates. The present investigation demonstrates that the spatial sectional method is capable of simulating the spatially nonhomogeneous acoustic agglomeration with high computation efficiency and numerical robustness and the coupling with flow dynamics will be the goal of future work.

A CFD-sectional algorithm for population balance equation coupled with multi-dimensional flow dynamics

“…Acoustic agglomeration, in recent years, has gained world-wide attention, as an efficient preconditioning technique in air conditioning systems [17,[50][51][52][53]. Under the action of a sound field, small particles in a suspension collide with one another to form large agglomerates and as a result, the efficiency of conventional particle removal technologies is greatly improved.…”

“…A variety of mechanisms have been proposed for the interaction between the sound field and suspended particles in aerosols and two primary mechanisms are generally accepted, which are the so-called orthokinetic and hydrodynamic interactions [17]. The acoustic kernels for two particles of diameter d1 and d2 which undergo orthorkinetic and hydrodynamic interactions in a standing wave field can be calculated by…”

“…the sectional method [15][16][17] and the fixed pivot method or cell average method [18][19][20]. The temporal model can explore some basic characteristics of the particulate system, however, its application is restricted by the homogeneity assumption to a limited number of scenarios since most of aerosol systems in industrial engineering or laboratory experiments are spatially inhomogeneous, i.e., multi-dimensional.…”

Investigation of CFD-PBM simulations based on fixed pivot method: Influence of the moment closure

“…Thus, population balance modeling (PBM) has been investigated by researchers all over the world to simulate the evolution of particulate systems in recent years [3]. The traditional population balance modeling assumes that the particulate system is homogeneous within the geometric boundaries, indicating that all the properties are identical everywhere without spatial variation of any quantity in the process [4,5]. However, such homogeneous PBM can only capture the temporal evolution of the particle size distribution (PSD), and hence is restricted to a limited number of scenarios since most of dispersed systems in industrial engineering are spatially inhomogeneous, i.e., multi-dimensional [6,7].…”

Modeling of particle interaction dynamics in standing wave acoustic field

“…Thus, increased research attention is being focused toward development of preconditioning technologies to enlarge PM 2.5 particles. Preconditioning technologies presently under development include acoustic agglomeration (Fan, Yang, and Kim 2013;Fan et al 2017;Hoffmann 2000;Sheng and Shen 2007;Shang et al 2018), electrical agglomeration (Chang et al 2017), magnetic agglomeration (Huang et al 2015;Zhao et al 2007), and heterogeneous condensation (Fan et al 2009a(Fan et al , 2009b(Fan et al , 2019a(Fan et al , 2019bSmith, Shaviv, and Svensmark 2016). Among these, acoustic agglomeration has attracted the greatest interest from researchers owing to its simple installation and ease of adaptation.…”