A Monte Carlo simulation of the engine development process

1983 ◽  
Author(s):  
W. ALLEY, JR. ◽  
H. STETSON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Development Process ◽  
Engine Development

Monte Carlo simulation of the engine development process

1984 ◽  
Vol 21 (7) ◽  
pp. 462-468 ◽  
Author(s):  
D. G. Culy ◽  
J. J. Gossen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Development Process ◽  
Engine Development

Optimisation and Robust Design in Aero Engine Development

2004 ◽  
Author(s):  
Alexander Karl ◽  
Stephan Lisiewicz ◽  
Winfried-Hagen Friedl ◽  
Janet Worgan ◽  
Gordon May
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Response Surface ◽  
Robust Design ◽  
Design Methods ◽  
Surface Model ◽  
Widespread Application ◽  
Deterministic Optimization ◽  
Aero Engine ◽  
Engine Development

Recent developments in computer capabilities and software enabled the application of deterministic optimization and Robust Design methods in real world aero engine development programs. This paper describes the methods used and shows several applications of this technology. The first example is the application of a Monte-Carlo simulation to support design decisions in the HP turbine casing air system. Here the main goal was to achieve a robust design addressing the variation of build tolerances on flow areas. The variation of parameters as mass flows, pressures and temperatures based on 5000 permutations of the base model give a high confidence level for achieving reliable system behavior for a large population of engines. In addition, dependencies of result parameters on input variations indicate the main levers for system improvement. A second example is the optimization of compressor discs. Here the main emphasis was on the influence of manufacturing tolerances and on the best method to evaluate these tolerances for longer running analysis tasks. Therefore, results of a full Monte-Carlo simulation are compared with results based on two surrogate models, a response surface and a Taylor series expansion. As a final example the optimization of a HP turbine disc for which a Design of Experiment has been performed to generate a response surface model is discussed. Using the response surface data the life variability due to assumptions in the thermal modeling have been quantified and used to adjust the constraints for the subsequent deterministic optimization for weight of the HP turbine. Using deterministic optimization and especially Robust Design methods a considerable decrease in development time and cost as well as an increased product quality and reliability have been achieved. However, deterministic optimization methods alone normally drive designs on to the constraint boundaries, leading to “cliff-edge” designs. Therefore, the application of Robust Design methods is required to increase the product reliability. These methods still require a considerable computing effort, so the widespread application is just starting.

Study on Effect of Neutral Grounding Mode on Reliability of Distribution Network

2012 ◽  
Vol 614-615 ◽  
pp. 719-723 ◽  
Author(s):  
Lei Dong ◽  
Lin He ◽  
Wei Di Li ◽  
Xiao Ao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Index ◽  
Optimization Problem ◽  
Development Process ◽  
Distribution Network ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Cable Lines ◽  
Matlab Programming

There exists an optimization problem of neutral grounding mode in distribution network with a large number of cable lines. In order to choose the best grounding mode for distribution network, factors of effect of neutral grounding mode on reliability of distribution network are analyzed. The development process of distribution line's fault under each neutral grounding mode is simulated with Monte Carlo simulation method by MATLAB programming. At the same time, the reliability index is got to evaluate and compare this effect quantitatively. An example is used to prove this method’s rationality and effectiveness.

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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