scholarly journals Low-Fidelity Model Mesh Density and the Performance of Variable-Resolution Shape Optimization Algorithms

2012 ◽  
Vol 9 ◽  
pp. 842-851 ◽  
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel ◽  
Stanislav Ogurtsov
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithms ◽  
Variable Resolution ◽  
Mesh Density ◽  
Fidelity Model

Adaptive response prediction for aerodynamic shape optimization

2017 ◽  
Vol 34 (5) ◽  
pp. 1485-1500
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Shape Optimization ◽  
Adaptive Response ◽  
Simulation Models ◽  
Response Prediction ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Aerodynamic Shape Optimization ◽  
Content Type ◽  
Aerodynamic Shape ◽  
Fidelity Model

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

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.

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