Model World: Tales from the Time Line—The Definition of OR and the Origins of Monte Carlo Simulation

2005 ◽  
Vol 35 (5) ◽  
pp. 429-435 ◽  
Author(s):  
Saul I. Gass ◽  
Arjang A. Assad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Line ◽  
Definition Of ◽  
Model World

Deriving Proven Oil Reserves Comments on the Monte Carlo Simulation Procedure

1993 ◽  
Vol 11 (1) ◽  
pp. 62-65
Author(s):  
Mark Wallace
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Basic Principles ◽  
Oil Reserves ◽  
Confidence Levels ◽  
Simulation Procedure ◽  
Probabilistic Distribution ◽  
Reservoir Description ◽  
Definition Of

The definition of reserves categories is frequently related directly back to the probabilistic distribution of reserves in the field. Most developments are planned around the P50 or “most likely” expectation for the field a level which incorporates the Proven plus Probable categories. The Proven category is usually backed out from the resulting reserves distribution by assuming an arbitrary P90 or P80 value, similarly upside or the Reserves including the Possible category are allocated a P20 or P10 value. This approach provides an “accepted” range to the reserves but is essentially reliant upon applying a range to a set of deterministric parameters. This approach assumes the basic principles of reservoir description are correct and can be applied at all confidence levels (P90-P10). In complex reservoirs this is less of a valid assumption, and running deterministic cases using pessimistic and optimistic data interpretations is the realistic way to determine the reserves range for the field.

Systematic Vibration Reliability Analysis of Random Parametered Gear Sets With Analytic and Multi-Crossing Monte Carlo Simulation Methods

2017 ◽  
Author(s):  
Ma Yupeng ◽  
Zhang Jianguo ◽  
Qiu Jiwei
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Vibration Response ◽  
Reliability Estimation ◽  
Driven Systems ◽  
Vibration Responses ◽  
Vibration Reliability ◽  
Definition Of ◽  
Nonstationary Stochastic Processes

Vibration reliability analysis of gear sets considering various kinds of nonlinear random factors is essential for the safety of gear driven systems. In this paper, a rational definition of gear sets vibration reliability was presented at first by taking all kinds of vibration responses including displacement, velocity and acceleration into account uniformly by treating them as a series system with statistically independent components. According to the given definition, a systematic analyzing scheme for the vibration reliability of gear sets was proposed. Vibration reliability estimated via the analyzing scheme would make it conservative but more safely in design of gear driven systems. Subsequently, both analytic and numerical methods for gear sets vibration response reliability estimation were carried out based on the proposed analyzing scheme. The analytic method is suitable for the situations that the vibration responses of gears sets under random circumstances are stationary stochastic responses. While, the numerical method named Multi-crossing Monte Carlo Simulation (MULCMCS) can well solve the reliability estimating problems even when the vibration responses of gear sets are nonstationary stochastic processes. Finally, for illustration, a numerical case of analyzing the vibration response reliability of a single degree-of-freedom (DOF) gear set was given to demonstrate the effectiveness of the MULCMCS method.

scholarly journals Accurate definition of control strategies using cross validated stepwise regression and Monte Carlo simulation

2019 ◽  
Vol 2 ◽  
pp. 100006
Author(s):  
Patrick Y. Yang ◽  
Cerintha J. Hui ◽  
Daniel J. Tien ◽  
Andrew W. Snowden ◽  
Gayle E. Derfus ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stepwise Regression ◽  
Control Strategies ◽  
Accurate Definition ◽  
Definition Of

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

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.

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