Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence

Giannakoglou, Kyriakos C.

doi:10.1016/s0376-0421(01)00019-7

Cited by 229 publications

(123 citation statements)

References 36 publications

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“…[6][7][8][9] EAs have been also applied to single-objective and multi-objective aerospace design optimization problems. 2,[10][11][12][13] This approach to finding many Pareto solutions works fine as it is, however, only when the number of objectives remains small (usually two, three at most, as shown in Fig. 3).…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Obayashi

Jeong

Chiba

et al. 2007

Trans. Japan Soc. Aero. S Sci.

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A new approach, Multi-Objective Design Exploration (MODE), is presented to address multidisciplinary design optimization (MDO) problems using computational fluid dynamics-computational structural dynamics (CFD-CSD) coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of the Kriging Model, adaptive range multi objective genetic algorithms, analysis of variance and a self-organizing map. The main emphasis of this approach is visual data mining. An MDO system using high-fidelity simulation codes, a Navier-Stokes solver and NASTRAN has been developed and applied to a regional-jet wing design. Because the optimization system becomes very expensive computationally, only brief exploration of the design space has been performed. However, visual data mining results demonstrate that design knowledge can produce a good design even after brief design exploration.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Obayashi

Jeong

Chiba

et al. 2007

Trans. Japan Soc. Aero. S Sci.

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“…Conclusions on the optimal combination of hierarchical and distributed search schemes are expected to be the same. Also, the RBF networks used as metamodels in this paper could be replaced by any other artificial neural network, response surface method, the kriging metamodel, etc (Giannakoglou 2002).…”

Section: Discussionmentioning

confidence: 99%

“…the metamodel. Metamodels (Giannakoglou 2002, Jin 2005, Karakasis and Giannakoglou 2005, Lim et al 2008, Zhou et al 2007, Emmerich et al 2006 interpolation or approximation methods such as polynomial regression, artificial neural networks, etc, which, after being trained on previously seen solutions, are used in place of E 1 to screen out non-promising candidate solutions at very low CPU cost. In this manner, only a few of the best offspring undergo evaluations on E i , i > 0.…”

Section: Distributed Hierarchical Evolutionary Algorithmmentioning

confidence: 99%

Distributed evolutionary algorithms with hierarchical evaluation

Kampolis

Giannakoglou

2009

Engineering Optimization

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“…Particularly, local models are used in favor of global models since constructing accurate global models is fundamentally flawed due to the curse of dimensionality [18][19][20][21]. Further, this allows more precise estimation on the unique characteristics of the problem landscapes, thus leading to the prediction on the appropriate level of localized model fidelity over the use of the original computationally expensive model.…”

Section: : End Whilementioning

confidence: 99%

Evolutionary Optimization with Dynamic Fidelity Computational Models

Lim

Ong

Jin

et al. 2008

Lecture Notes in Computer Science

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Abstract. In optimization, it is now a common practice to use lower fidelity computational models in place of the original model when dealing with problems with computationally expensive objective functions. In this paper, we present a study on evolutionary optimization with dynamic fidelity computational models capable of acclimatizing to localized complexity, for enhancing design search efficiency. In particular, we propose an evolutionary framework for model fidelity control that decides, at runtime, the appropriate fidelity level of the computational model, which is deemed to be computationally less expensive, to be used in place of the exact analysis code as the search progresses. Empirical study on an aerodynamic airfoil design problem based on a Memetic Algorithm with Dynamic Fidelity Model (MA-DFM) demonstrates that improved quality solution and efficiency are obtained over existing evolutionary schemes.

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Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence

Cited by 229 publications

References 36 publications

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Distributed evolutionary algorithms with hierarchical evaluation

Evolutionary Optimization with Dynamic Fidelity Computational Models

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