Multi-Monte Carlo approach for general dynamic equation considering simultaneous particle coagulation and breakage

Abstract Particle size distribution is described by general dynamic equation (GDE). A new multi-Monte Carlo (MMC) method is promoted to solve GDE for simultaneous particle coagulation and breakage. MMC method is based on “time-driven” Monte Carlo technique and conserves constant number of fictitious particles and constant volume of computational domain with the evolution of time. Firstly, MMC method is described in details, which includes the scheme of simultaneous coagulation and breakage, the introduction of “weighted fictitious particle”, the setting of time step, the judgment of the occurrence of coagulation and breakage event, the choice of fictitious coagulation partner, and dealing with the consequence of particle coagulation and breakage event. Then MMC method is used to simulate four kinds of special cases in which complete or partial analytical solutions exist; the simulation results of MMC method for GDE agree with analytical solutions well, which proves that MMC method has high and stable statistical precision.

[1]  P. J. Blatz,et al.  Note on the Kinetics of Systems Manifesting Simultaneous Polymerization-Depolymerization Phenomena , 1945 .

[2]  Katsuhisa Koura,et al.  Comment on “Direct Simulation Scheme Derived from the Boltzmann Equation. I. Monocomponent Gases” , 1981 .

[3]  Alejandro L. Garcia,et al.  A Monte Carlo simulation of coagulation , 1987 .

[4]  Andreas Eibeck,et al.  Approximative solution of the coagulation–fragmentation equation by stochastic particle systems , 2000 .

[5]  Karl K. Sabelfeld,et al.  Stochastic algorithms for solving Smolouchovsky coagulation equation and applications to aerosol growth simulation. , 1996, Monte Carlo Methods Appl..

[6]  Margaritis Kostoglou,et al.  On the steady-state size distribution of dispersions in breakage processes , 1997 .

[7]  Vanni,et al.  Approximate Population Balance Equations for Aggregation-Breakage Processes. , 2000, Journal of colloid and interface science.

[8]  W. Gene Tucker,et al.  An overview of PM2.5 sources and control strategies , 2000 .

[9]  Themis Matsoukas,et al.  Constant-number Monte Carlo simulation of population balances , 1998 .

[10]  Markus Kraft,et al.  Simulation of coalescence and breakage: an assessment of two stochastic methods suitable for simulating liquid–liquid extraction , 2004 .

[11]  Kangtaek Lee,et al.  Simultaneous coagulation and break-up using constant-N Monte Carlo , 2000 .

[12]  Richard C. Flagan,et al.  A discrete-sectional solution to the aerosol dynamic equation , 1988 .

[13]  J. Seinfeld,et al.  Size‐resolved and chemically resolved model of atmospheric aerosol dynamics , 1998 .

[14]  Wolfgang Wagner,et al.  An Efficient Stochastic Algorithm for Studying Coagulation Dynamics and Gelation Phenomena , 2000, SIAM J. Sci. Comput..

[15]  Chuguang Zheng,et al.  Multi-Monte Carlo method for particle coagulation: description and validation , 2005, Appl. Math. Comput..

[16]  Wayne Seames,et al.  An initial study of the fine fragmentation fly ash particle mode generated during pulverized coal combustion , 2003 .

[17]  Chuguang Zheng,et al.  Multi-Monte Carlo method for coagulation and condensation/evaporation in dispersed systems. , 2005, Journal of colloid and interface science.

[18]  Kangtaek Lee,et al.  Solution of the population balance equation using constant-number Monte Carlo , 2002 .

[19]  F. Einar Kruis,et al.  Direct simulation Monte Carlo method for particle coagulation and aggregation , 2000 .

[20]  Renwei Mei,et al.  Particle collision rate in fluid flows , 1998 .

[21]  Kurt Liffman,et al.  A direct simulation Monte-Carlo method for cluster coagulation , 1992 .

[22]  Bijay K. Mishra Monte Carlo simulation of particle breakage process during grinding , 2000 .

[23]  L. Steiner,et al.  Monte Carlo simulation of drop dispersion behaviour , 2000 .

[24]  S. Friedlander,et al.  The self-preserving particle size distribution for coagulation by brownian motion☆ , 1966 .

[25]  Benjamin Jourdain,et al.  A stochastic approach for the numerical simulation of the general dynamics equation for aerosols , 2003 .

[26]  Sotiris E. Pratsinis,et al.  Gas-phase manufacture of particulates: interplay of chemical reaction and aerosol coagulation in the free-molecular regime , 1989 .

[27]  Andreas Eibeck,et al.  Stochastic Particle Approximations for Smoluchoski’s Coagualtion Equation , 2001 .

[28]  J. H. Olson,et al.  A moment methodology for coagulation and breakage problems: Part 1—analytical solution of the steady-state population balance , 2002 .

[29]  P. Das Monte Carlo simulation of drop breakage on the basis of drop volume , 1996 .

[30]  D. P. Patil,et al.  An analytical solution to continuous population balance model describing floc coalescence and breakage : A special case , 1998 .