Surrogate-based analysis and optimization

Queipo, Néstor V.; Haftka, Raphael T.; Shyy, Wei; Goel, Tushar; Vaidyanathan, Rajkumar; Tucker, P. Kevin

doi:10.1016/j.paerosci.2005.02.001

Cited by 2,026 publications

(1,113 citation statements)

References 79 publications

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“…An overview of surrogate-based analysis and optimization was presented in this journal by Queipo et al [61]. They covered some of the most popular methods in design space sampling, surrogate model construction, model selection and validation, sensitivity analysis, and surrogate-based optimization.…”

Section: Introductionmentioning

confidence: 99%

Recent advances in surrogate-based optimization

Forrester

Keane

2009

Progress in Aerospace Sciences

1,916

954

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The evaluation of aerospace designs is synonymous with the use of long running and computationally intensive simulations. This fuels the desire to harness the efficiency of surrogate-based methods in aerospace design optimization. Recent advances in surrogate-based design methodology bring the promise of efficient global optimization closer to reality. We review the present state of the art of constructing surrogate models and their use in optimization strategies. We make extensive use of pictorial examples and, since no method is truly universal, give guidance as to each method's strengths and weaknesses.

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Section: Introductionmentioning

confidence: 99%

Recent advances in surrogate-based optimization

Forrester

Keane

2009

Progress in Aerospace Sciences

1,916

954

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“…MDO is a rapidly growing area of research. 1,2) Typical MDO problems involve competing objectives; for example, in aircraft design, the minimization of aerodynamic drag, minimization of structural weight, etc. While singleobjective problems may have a unique optimal solution, multi-objective problems (MOPs) have a set of compromised solutions, largely known as the trade-off surface, Pareto-optimal solutions or non-dominated solutions.…”

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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“…= lift, drag, and pitching moment coefficients O VER the past two decades, surrogate-based modeling and optimization have played an increasingly important role in different areas of aerospace engineering [1][2][3][4][5][6][7][8], such as aerodynamic design optimization [9,10], structural optimization [11], and multidisciplinary design optimization of aircraft or spacecraft [12], where high-fidelity and expensive numerical simulations, such as computational fluid dynamics (CFD) or computational solid dynamics (CSD), are often employed. Surrogate model is also called "response surface model", "metamodel", "approximation model", or "emulator" by the literature in different research areas.…”

mentioning

confidence: 99%

“…Therefore, the applications of this method are usually limited to either two-dimensional aerodynamic configurations [61] or three-dimensional (3-D) configurations using low-fidelity and fast CFD methods [62]. And the third approach is surrogate-based optimization (SBO) [1][2][3][4][5][6][7][8][9]. As previously mentioned, the surrogate models enable us to find the global optimum within a very limited number of expensive evaluations [30,38].…”

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confidence: 99%

Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization

et al. 2017

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A novel formulation of gradient-enhanced surrogate model, called weighted gradient-enhanced kriging, is proposed and used in combination with the cheap gradients obtained by the adjoint method to ameliorate the "curse of dimensionality". The core idea is to build a series of submodels with much smaller correlation matrices and then sum them up with appropriate weight coefficients, aiming to avoid the prohibitive cost associated with decomposing the large correlation matrix of a gradient-enhanced kriging. A self-contained derivation of the proposed method is presented, and then it is verified by surrogate modeling test cases. The present method is integrated into a surrogatebased optimizer and tested for design optimizations. It is further demonstrated for inverse design of a transonic wing, parameterized with a number of design variables in the range from 36 to 108, using Reynolds-averaged Navier-Stokes flow and adjoint solvers. It is observed that, for the wing design with 36 and 54 variables, the weighted and conventional gradient-enhanced kriging are comparable, and both are much more efficient than kriging without using any gradient. For the wing design with 72 and 108 variables, the cost of training a gradient-enhanced kriging increases rapidly and becomes prohibitive. In contrast, the cost of training a weighted gradient-enhanced kriging is kept in an acceptable level, which makes it more practical for higher-dimensional problems.

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Surrogate-based analysis and optimization

Abstract: A major challenge to the successful full-scale development of modem aerospace

Cited by 2,026 publications

References 79 publications

Recent advances in surrogate-based optimization

Recent advances in surrogate-based optimization

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

Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization

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