TH-EF-204-03: Determination of Small Field Output Factors, Advantages and Limitations of Monte Carlo Simulation

2016 ◽  
Vol 43 (6Part47) ◽  
pp. 3893-3893
Author(s):  
J. Puxeu Vaque
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Small Field ◽  
Output Factors

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

scholarly journals Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation

2021 ◽  
Vol 26 ◽  
pp. 100862
Author(s):  
Abrar Hussain ◽  
Lihao Yang ◽  
Shifeng Mao ◽  
Bo Da ◽  
Károly Tőkési ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Precision ◽  
Backscattering Coefficient ◽  
Electron Backscattering

Determination of a general economic location constant by monte carlo simulation

1974 ◽  
Vol 1 (1) ◽  
pp. 119-124
Author(s):  
T.L. Honeycutt ◽  
C.E. Grad ◽  
J.D. Wilson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo

Determination of the Residual Entropy of Simple Mixtures by Monte Carlo Simulation

Langmuir ◽  
2017 ◽  
Vol 33 (42) ◽  
pp. 11603-11610 ◽  
Author(s):  
Eric Detmar ◽  
Simin Yazdi Nezhad ◽  
Ulrich K. Deiters
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Residual Entropy

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

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