scholarly journals Scaling Properties of Multi-Fidelity Shape Optimization Algorithms

2012 ◽  
Vol 9 ◽  
pp. 832-841 ◽  
Author(s):  
Slawomir Koziel ◽  
Leifur Leifsson
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithms ◽  
Scaling Properties

A Shape Parameterization Method Using Principal Component Analysis in Applications to Parametric Shape Optimization

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
Kazuo Yonekura ◽  
Osamu Watanabe
Keyword(s):  
Principal Component Analysis ◽  
Shape Optimization ◽  
Parametric Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Principal Component ◽  
Component Analysis ◽  
Parameterization Method ◽  
Shape Parameterization ◽  
Set Of Points

This paper proposes a shape parameterization method using a principal component analysis (PCA) for shape optimization. The proposed method is used as a preprocessing tool of parametric optimization algorithms, such as genetic algorithms (GAs) or response surface methods (RSMs). When these parametric optimization algorithms are used, the number of parameters should be small while the design space represented by the parameters should be able to represent a variety of shapes. In order to define the parameters, PCA is applied to shapes. In many industrial fields, a large amount of data of shapes and their performance is accumulated. By applying PCA to these shapes included in a database, important features of the shapes are extracted. A design space is defined by basis vectors which are generated from the extracted features. The number of dimensions of the design space is decreased without omitting important features. In this paper, each shape is discretized by a set of points and PCA is applied to it. A shape discretization method is also proposed and numerical examples are provided.

Comparing Optimization Algorithms for Shape Optimization of Extrusion Dies

PAMM ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 789-794 ◽  
Author(s):  
Roland Siegbert ◽  
Johannes Kitschke ◽  
Hatim Djelassi ◽  
Marek Behr ◽  
Stefanie Elgeti
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithms ◽  
Extrusion Dies

scholarly journals Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
U. Selvakumar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Aerodynamic Shape Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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