An efficienct stochastic algorithm for simulating nano-particle dynamics

Journal of Computational Physics

Volumn 183, Issue 1, 2002, Pages 210-232

  Goodson, Michael ^a   Kraft, Markus ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Coalescence; Nano particle dynamics; Population balance equation; Stochastic modelling

Indexed keywords

COALESCENCE; DYNAMICS; MARKOV PROCESSES; MONTE CARLO METHODS; NANOPARTICLES; SILICA; STOCHASTIC SYSTEMS;

DIRECT SIMULATION MONTE CARLO; MAJORANT; MONTE CARLO ALGORITHMS; NANO-PARTICLE DYNAMIC; NANOPARTI-CLES; PARTICLE DYNAMICS; PARTICLE NUMBERS; POPULATION-BALANCE EQUATIONS; STOCHASTIC ALGORITHMS; STOCHASTIC-MODELING;

EFFICIENCY;

EID: 0037146443     PISSN: 00219991     EISSN: None     Source Type: Journal    
DOI: 10.1006/jcph.2002.7192     Document Type: Article

References (20)

1. A. Eibeck and W. Wagner, An efficient stochastic algorithm for studying coagulation dynamics and gelation phenomena, SIAM J. Sci. Comput. 22(3), 802 (2000).
2. A. Eibeck and W. Wagner, Stochastic particle approximations for Smoluchowski’s coagulation equation, Ann. Appl. Prohab. 11(4), 1137 (2001).
3. M. Frenklach, Dynamics of discrete distribution for Smoluchowski coagulation model, J. Colloid Interface Sci. 108(1), 237 (1985).
4. M. Frenklach and H. Wang, Detailed mechanism and modeling of soot particle formation, in Soot Formation in Combustion - Mechanisms and Models, edited by H. Bockhorn (Springer-Verlag, Berlin/New York, 1994), pp.165-192.
5. S. K. Friedlander, Smoke, Dust and Haze (Wiley, New York, 1977).
6. D. T. Gillespie, The stochastic coalescence model for cloud droplet growth, J. Atmos. Sci. 29, 1496 (1972).
7. D. T. Gillespie, An exact method for numerically simulating the stochastic coalescence process in a cloud, J. Atmos. Sci. 32(10), 1977 (1975).
8. M. J. Hounslow, R. L. Ryall, and V. R. Marshall, A discretized population balance for nucleation, growth and aggregation, AIChE J. 34(11), 1821 (1988).
9. M. J. Hounslow, A discretised population balance for continuous systems at steady state, AIChE J. 36(1), 106 (1990).
10. H. M. Hulbert and S. Katz, Some problems in particle technology. A statistical mechanical formulation, Chem. Eng. Sci. 19, 555 (1964).
11. M. Kraft and W. Wagner, Numerical Study of a Stochastic Particle Method for Homogeneous Gas Phase Reactions, Technical report 570, Weierstrass Institute for Applied Analysis and Stochastics (2000).
12. T. Matsoukas and S. K. Friedlander, Dynamics of aerosol agglomerates formation, J. Colloid Sci. 146(2), 495 (1991).
13. S. Panda and S. E. Pratsinis, Modeling the synthesis of aluminum particles by evaporation-condensation in an aerosol flow reactor, Nanostructured Mater. 5(7-8), 755 (1995).
14. D. Ramkrishna, Population Balances. Theory and Applications to Particulate Systems in Engineering (Academic Press, San Diego, 2000).
15. A. D. Randolph and M. A. Larson, Theory of Particulate Processes (Academic Press, London, 1988).
16. K. K. Sabelfeld, S. V. Rogasinsky, A. A. Kolodko, and A. I. Levykin, Stochastic algorithms for solving Smoluchovsky coagulation equation and applications to aerosol growth simulation, Monte Carlo Meth. Appl. 2(1), 41 (1996).
17. M. Smith and T. Matsoukas, Constant-number Monte Carlo simulation of population balances, Chem. Eng. Sci. 53(9), 1777 (1998).
18. M. von Smoluchowski, Drei Vorträge über Diffusion, Brownsche Molekularbewegung und Koagulation von Kolloidteilchen, Phys. Z. 17, 557; 585 (1916).
19. K. T. Whitby, Determination of aerosol growth-rates in the atmosphere using lumped mode aerosol dynamics, J. Aerosol Sci. 12(3), 173 (1981).
20. M. R. Zachariah and H. G. Semerjian, Simulation of ceramic particle formation: Comparison with in-situ measurements, AIChE J. 35(12), 2003 (1989).