scholarly journals A Monte Carlo Framework to Simulate Multicomponent Droplet Growth by Stochastic Coalescence

10.5772/14522 ◽  
2011 ◽  
Author(s):  
Lester Alfonso ◽  
Graciela Raga ◽  
Darrel Baumgardner
Keyword(s):  
Monte Carlo ◽  
Droplet Growth ◽  
Stochastic Coalescence ◽  
Monte Carlo Framework

Coarse-grained modeling of the nucleation of polycyclic aromatic hydrocarbons into soot precursors

2019 ◽  
Vol 21 (9) ◽  
pp. 5123-5132 ◽  
Author(s):  
J. Hernández-Rojas ◽  
F. Calvo
Keyword(s):  
Monte Carlo ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Physical Growth ◽  
Coarse Grained ◽  
Soot Precursors ◽  
Polycyclic Aromatic ◽  
Hydrocarbon Molecules ◽  
A Minor ◽  
Monte Carlo Framework

The aggregation and physical growth of polycyclic aromatic hydrocarbon molecules was simulated using a coarse-grained potential and a stochastic Monte Carlo framework. In agreement with earlier studies, homomolecular nucleation of pyrene, coronene and circumcoronene is found to be limited at temperatures in the 500–1000 K range. Heteromolecular nucleation is found to occur with a minor spontaneous segregation toward pure and equi concentrations.

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

scholarly journals Monte Carlo simulations of two-component drop growth by stochastic coalescence

2008 ◽  
Vol 8 (2) ◽  
pp. 7289-7313 ◽  
Author(s):  
L. Alfonso ◽  
G. B. Raga ◽  
D. Baumgardner
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Stochastic Algorithm ◽  
Aerosol Composition ◽  
Particle Charging ◽  
Two Component ◽  
Different Types ◽  
Framework Species ◽  
Stochastic Coalescence ◽  
Good Agreement

Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method.~The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by comparing the numerical with the analytical solutions found by Lushnikov (1975). Very good agreement was observed between the Monte Carlo simulations and the analytical solution. Simulation results are presented for bi-variate constant and hydrodynamic kernels. The algorithm can be easily extended to incorporate various properties of clouds such as including several crystal habits, different types of soluble CCN, particle charging and drop breakup.

Introduction — A Monte Carlo Framework

2015 ◽  
pp. 105-106
Author(s):  
Roland Lichters ◽  
Roland Stamm ◽  
Donal Gallagher
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Framework
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