Optimal Operation of Batch Processes under Uncertainty: A Monte Carlo Simulation-Deterministic Optimization Approach

2003 ◽  
Vol 42 (26) ◽  
pp. 6815-6822 ◽  
Author(s):  
Ioannis K. Kookos
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Optimal Operation ◽  
Batch Processes ◽  
Optimization Approach ◽  
Deterministic Optimization

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.

DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts

2019 ◽  
Vol 34 (04) ◽  
pp. 1950032 ◽  
Author(s):  
Gaurav Dhiman ◽  
Pritpal Singh ◽  
Harsimran Kaur ◽  
Ritika Maini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
State Of The Art ◽  
Real Life ◽  
Optimization Method ◽  
Optimization Approach ◽  
Effective Number ◽  
Number Of Solutions ◽  
Efficient Prediction ◽  
Competing Models

This paper presents a new model using optimization approach for efficient prediction of load in real-life environment. Monte Carlo simulation and Schrödinger equations provide the effective number of solutions. This technique is useful in representation of relationships between different models. The proposed algorithm is verified and validated with various state-of-the-art approaches for solving economic load power dispatch problem to demonstrate its efficiency. Experimental results signify that the proposed algorithm is more precise than existing competing models.

Efficient Gradient-Based Tolerance Optimization Using Monte Carlo Simulation

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
R. Alan Bowman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Functional Requirements ◽  
Mechanical Assembly ◽  
New Approach ◽  
Tolerance Optimization ◽  
Gradient Based ◽  
Derivatives Of

A gradient-based optimization approach is employed to select design tolerances for the component dimensions of a mechanical assembly to minimize manufacturing cost while achieving a desired probability of meeting functional requirements, known as the yield. Key to the feasibility of such an approach is to be able to use Monte Carlo simulation to make estimates of the derivatives of the yield with respect to the design tolerances quickly and accurately. A new approach for making these estimates is presented and is shown to be far faster and more accurate than previous approaches. Gradient-based optimization using the new approach for estimating the derivatives is applied to example problems from the literature. The solutions are superior to all previously published solutions and are obtained with very reasonable computer run times. Additional advantages of a gradient-based approach are described.

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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