Effect of Ambient Pressure on the Generation and the Propagation of Plasmas Produced by Pulsed Laser Ablation of Metal Al in Air

1997 ◽  
Vol 51 (12) ◽  
pp. 1910-1915 ◽  
Author(s):  
B. Y. Man ◽  
X. T. Wang ◽  
G. T. Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ambient Pressure ◽  
Pulsed Laser ◽  
Emission Spectra ◽  
Pulsed Laser Ablation ◽  
Quantitative Information ◽  
Slowing Down ◽  
The Mean ◽  
Monte Carlo Simulation Program

Simultaneous time- and space-resolved emission spectra from a Nd: YAG laser-ablated plasma on an Al target have yielded quantitative information on the generation and the propagation of species in the plasma. Five ambient pressures from atmosphere to 5 Pa were chosen. The experimental results showed that the characteristics of the species in the plasma were strongly influenced by the ambient pressure. The mean spatially projected ranges of Al2+ ions in the atmosphere of different densities were calculated in theory, and the slowing-down processes were simulated by using a popular Monte Carlo simulation program, TRIM90. The calculated results can be used to explain observed experimental phenomena.

Spectrum analysis of plasma produced by pulsed laser ablation of GaAs

2007 ◽  
Vol 73 (2) ◽  
pp. 231-239 ◽  
Author(s):  
AI HUA LIU
Keyword(s):  
Laser Ablation ◽  
Ambient Pressure ◽  
Pulsed Laser ◽  
Emission Spectra ◽  
Diagnostic Technique ◽  
Pulsed Laser Ablation ◽  
Stark Broadening ◽  
Experimental Conditions ◽  
Time Resolved ◽  
Excited Atoms

Abstract.A time-resolved diagnostic technique was used to investigate the emission spectra from the plasmas produced by 1.06 μm, 10 ns pulsed laser ablation of semiconductor GaAs. The characteristics of the species in plasma produced at different ambient pressure were analyzed. The full width at half maximum of the spectral line was measured and analyzed according to obtained spectra of the excited atoms; several line broadening factors were estimated according to our experimental conditions and the results indicate that the Stark broadening is the main broadening mechanism. Under the assumption of local thermodynamic equilibrium, the time evolution of electron number density was deduced from the Stark broadening measurements.

Monte Carlo simulation of pulsed laser ablation into an ambient gas

1998 ◽  
Vol 10 (1-4) ◽  
pp. 144-147 ◽  
Author(s):  
T.E. Itina ◽  
W. Marine ◽  
M. Autric
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Laser Ablation ◽  
Pulsed Laser ◽  
Pulsed Laser Ablation ◽  
Ambient Gas

Monte Carlo simulation of the subsurface growth mode during pulsed laser deposition

2011 ◽  
Vol 110 (4) ◽  
pp. 043304 ◽  
Author(s):  
M. R. Rashidian Vaziri ◽  
F. Hajiesmaeilbaigi ◽  
M. H. Maleki
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Pulsed Laser Deposition ◽  
Pulsed Laser ◽  
Laser Deposition ◽  
Growth Mode

Kinetic Monte Carlo Simulation of Impurity Effects on Nucleation and Growth of SiC Clusters on Si(100)

2013 ◽  
Vol 740-742 ◽  
pp. 393-396
Author(s):  
Maxim N. Lubov ◽  
Jörg Pezoldt ◽  
Yuri V. Trushin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cluster Size ◽  
Kinetic Monte Carlo ◽  
Nucleation And Growth ◽  
Nucleation Density ◽  
Nucleation Process ◽  
Impurity Effects ◽  
Kinetic Monte Carlo Simulations ◽  
The Mean

The influence of attractive and repulsive impurities on the nucleation process of the SiC clusters on Si(100) surface was investigated. Kinetic Monte Carlo simulations of the SiC clusters growth show that that increase of the impurity concentration (both attractive and repulsive) leads to decrease of the mean cluster size and rise of the nucleation density of the clusters.

scholarly journals ANALISIS PENJADWALAN PROYEK GEDUNG BERTINGKAT DENGAN METODE PERT DAN M-PERT MENGGUNAKAN SIMULASI MONTE CARLO

2020 ◽  
Vol 3 (3) ◽  
pp. 533
Author(s):  
Josua Guntur Putra ◽  
Jane Sekarsari
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Research Method ◽  
Probabilistic Approach ◽  
Deterministic Scheduling ◽  
Work Duration ◽  
The Mean ◽  
Building Project ◽  
Story Building

One of the keys to success in construction execution is timeliness. In fact, construction is often late than originally planned. It’s caused by project scheduling uncertainty. Deterministic scheduling methods use data from previous projects to determine work duration. However, not every project has same work duration. The PERT method provides a probabilistic approach that can overcome these uncertainties, but it doesn’t account for the increase in duration due to parallel activities. In 2017, the PERT method was developed into the M-PERT method. The purpose of this study is to compare the mean duration and standard deviation of the overall project between PERT and M-PERT methods and compare them in Monte Carlo simulation. The research method used is to calculate the mean duration of the project with the PERT, M-PERT, and Monte Carlo simulation. The study was applied to a three-story building project. From the results of the study, the standard deviation obtained was 5.079 for the M-PERT method, 8.915 for the PERT method, and 5.25 for the Monte Carlo simulation. These results show the M-PERT method can provide closer results to computer simulation result than the PERT method. Small standard deviation value indicates the M-PERT method gives more accurate results.ABSTRAKSalah satu kunci keberhasilan dalam suatu pelaksanaan konstruksi adalah ketepatan waktu. Kenyataannya, pelaksanaan konstruksi sering mengalami keterlambatan waktu dari yang direncanakan. Hal ini disebabkan oleh ketidakpastian dalam merencanakan penjadwalan proyek. Metode penjadwalan yang bersifat deterministik menggunakan data dari proyek sebelumnya untuk menentukan durasi pekerjaan. Akan tetapi, tidak setiap proyek memiliki durasi pekerjaan yang sama. Metode PERT memberikan pendekatan probabilistik yang dapat mengatasi ketidakpastian tersebut, tetapi metode ini tidak memperhitungkan pertambahan durasi akibat adanya kegiatan yang berbentuk paralel. Pada tahun 2017, metode PERT dikembangkan menjadi metode M-PERT. Tujuan dari penelitian ini adalah membandingkan mean durasi dan standar deviasi proyek secara keseluruhan antara metode PERT dan M-PERT dan membandingkan kedua metode tersebut dalam simulasi Monte Carlo. Metode penelitian yang dilakukan adalah menghitung mean durasi proyek dengan metode PERT, M-PERT, dan simulasi Monte Carlo. Penelitian diterapkan pada proyek gedung bertingkat tiga. Dari hasil penelitian, nilai standar deviasi diperoleh sebesar 5,079 untuk metode M-PERT, 8,915 untuk metode PERT, dan 5,25 untuk simulasi Monte Carlo. Hasil ini menunjukan metode M-PERT dapat memberikan hasil yang lebih mendekati hasil simulasi komputer daripada metode PERT. Nilai standar deviasi yang kecil menunjukan metode M-PERT memberikan hasil yang lebih akurat.

scholarly journals STARlight: A Monte Carlo simulation program for ultra-peripheral collisions of relativistic ions

2017 ◽  
Vol 212 ◽  
pp. 258-268 ◽  
Author(s):  
Spencer R. Klein ◽  
Joakim Nystrand ◽  
Janet Seger ◽  
Yuri Gorbunov ◽  
Joey Butterworth
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Program ◽  
Peripheral Collisions ◽  
Monte Carlo Simulation Program

scholarly journals Statistical Mechanics of Discrete Multicomponent Fragmentation

2020 ◽  
Vol 5 (4) ◽  
pp. 64
Author(s):  
Themis Matsoukas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Partition Function ◽  
Statistical Mechanics ◽  
Closed Form ◽  
Fixed Number ◽  
Arbitrary Degree ◽  
Fragment Distribution ◽  
The Mean ◽  
Fragmentation Event

We formulate the statistics of the discrete multicomponent fragmentation event using a methodology borrowed from statistical mechanics. We generate the ensemble of all feasible distributions that can be formed when a single integer multicomponent mass is broken into fixed number of fragments and calculate the combinatorial multiplicity of all distributions in the set. We define random fragmentation by the condition that the probability of distribution be proportional to its multiplicity, and obtain the partition function and the mean distribution in closed form. We then introduce a functional that biases the probability of distribution to produce in a systematic manner fragment distributions that deviate to any arbitrary degree from the random case. We corroborate the results of the theory by Monte Carlo simulation, and demonstrate examples in which components in sieve cuts of the fragment distribution undergo preferential mixing or segregation relative to the parent particle.

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