Monte Carlo Simulation of Particle Interactions at High Dynamic Range: Advancing beyond the Googol

We present a method which extends Monte Carlo studies to situations that require a large dynamic range in particle number. The underlying idea is that, in order to calculate the collisional evolution of a system, some particle interactions are more important than others and require more resolution, while the behavior of the less important, usually of smaller mass, particles can be considered collectively. In this approximation, groups of identical particles, sharing the same mass and structural parameters, operate as one unit. The amount of grouping is determined by the zoom factor?a free parameter that determines on which particles the computational effort is focused. Two methods for choosing the zoom factors are discussed: the equal-mass method, in which the groups trace the mass density of the distribution, and the distribution method, which additionally follows fluctuations in the distribution. Both methods achieve excellent correspondence with analytic solutions to the Smoluchowski coagulation equation. The grouping method is furthermore applied to simulations involving runaway kernels, where the particle interaction rate is a strong function of particle mass, and to situations that include catastrophic fragmentation. For the runaway simulations, previous predictions for the decrease of the runaway timescale with the initial number of particles $m{c N} $ --> are reconfirmed, extending $m{c N} $ --> to 10160. Astrophysical applications include modeling of dust coagulation, planetesimal accretion, and the dynamical evolution of stars in large globular clusters. The proposed method is a powerful tool to compute the evolution of any system where the particles interact through discrete events, with the particle properties characterized by structural parameters.

[1]  Monte Carlo results for random coagulation , 1985 .

[2]  I J Laurenzi,et al.  Kinetics of random aggregation-fragmentation processes with multiple components. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Simon F. Portegies Zwart,et al.  A runaway collision in a young star cluster as the origin of the brightest supernova , 2007, Nature.

[4]  J. Silk,et al.  A statistical model for the initial stellar mass function , 1979 .

[5]  Man Hoi Lee A survey of numerical solutions to the coagulation equation , 2001 .

[6]  A. Tielens,et al.  Dust coagulation in protoplanetary disks: porosity matters , 2006, astro-ph/0610030.

[7]  Man Hoi Lee,et al.  On the Validity of the Coagulation Equation and the Nature of Runaway Growth , 2000 .

[8]  L. Malyshkin,et al.  The Timescale of Runaway Stochastic Coagulation , 2001 .

[9]  William K. Hartmann,et al.  Planetesimals to planets: Numerical simulation of collisional evolution , 1978 .

[10]  J. Camacho Scaling in steady-state aggregation with injection. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  W. White On the form of steady-state solutions to the coagulation equations , 1982 .

[12]  Daniel T. Gillespie,et al.  An Exact Method for Numerically Simulating the Stochastic Coalescence Process in a Cloud , 1975 .

[13]  Planet formation by coagulation: A focus on Uranus and Neptune , 2004, astro-ph/0405215.

[14]  James D. Klett,et al.  A Class of Solutions to the Steady-State, Source-Enhanced, Kinetic Coagulation Equation , 1975 .

[15]  Runaway collisions in young star clusters – I. Methods and tests , 2005, astro-ph/0503129.

[16]  A. Kovetz,et al.  The Effect of Coalescence and Condensation on Rain Formation in a Cloud of Finite Vertical Extent , 1969 .

[17]  F. Leyvraz,et al.  Scaling theory and exactly solved models in the kinetics of irreversible aggregation , 2003, cond-mat/0305670.

[18]  S. Kenyon,et al.  A Hybrid N-Body-Coagulation Code for Planet Formation , 2006, astro-ph/0602327.

[19]  T. Henning,et al.  N-Particle-Simulations of Dust Growth: I. Growth Driven by Brownian Motion , 1999 .

[20]  T. Henning,et al.  Coagulation, fragmentation and radial motion of solid particles in protoplanetary disks , 2007, 0711.2192.

[21]  T. Abel,et al.  Resolving the Formation of Protogalaxies. I. Virialization , 2007, 0804.4156.

[22]  F. Mokler,et al.  Charge-induced gelation of microparticles , 2005 .

[23]  Intae Jeon,et al.  Existence of Gelling Solutions for Coagulation-Fragmentation Equations , 1998 .

[24]  Francesco Marzari,et al.  Accretional evolution of a planetesimal swarm: 1. A new simulation , 1991 .

[25]  Simon Portegies Zwart,et al.  Monte Carlo Simulations of Globular Cluster Evolution. I. Method and Test Calculations , 2000 .

[26]  J. Cuzzi,et al.  Solving the Coagulation Equation by the Moments Method , 2008, 0804.4189.

[27]  Ricard V. Solé,et al.  Scaling in ecological size spectra , 2001 .

[28]  Validity of the statistical coagulation equation and runaway growth of protoplanets , 1994 .

[29]  H. Hayakawa Irreversible kinetic coagulations in the presence of a source , 1987 .

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

[31]  E Bonabeau,et al.  Scaling in animal group-size distributions. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[32]  George W. Wetherill,et al.  Comparison of analytical and physical modeling of planetesimal accumulation , 1990 .

[33]  C. Dominik,et al.  Dust coagulation in protoplanetary disks: A rapid depletion of small grains , 2004, astro-ph/0412117.

[34]  J. Cuzzi,et al.  Closed-form expressions for particle relative velocities induced by turbulence , 2007, astro-ph/0702303.

[35]  Scott L. Diamond,et al.  A General Algorithm for Exact Simulation of Multicomponent Aggregation Processes , 2002 .