Multiple Surrogates for the Shape Optimization of Bluff Body-facilitated Mixing

Mack, Yolanda; Goel, Tushar; Shyy, Wei; Haftka, Raphael T.; Queipo, Néstor V.

doi:10.2514/6.2005-333

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…However, it may be worth exploring the combination of several of them for exploiting either their global or their local nature. This idea has been explored in the past, for example, by Mack et al [55], by using a combination of polynomial respose surface methods and radial basis functions, for performing global sensitivity analysis and shape optimization of bluff bodies. Also, Glaz et al [28] adopted three approximation models, namely polynomial, kriging, and radial basis functions.…”

Section: Conclusion and Future Research Pathsmentioning

confidence: 99%

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Santana‐Quintero

Montano

Coello

2010

Evolutionary Learning and Optimization

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Abstract. Evolutionary algorithms have been very popular for solving multiobjective optimization problems, mainly because of their ease of use, and their wide applicability. However, multi-objective evolutionary algorithms (MOEAs) tend to consume an important number of objective function evaluations, in order to achieve a reasonably good approximation of the Pareto front. This is a major concern when attempting to use MOEAs for real-world applications, since we can normally afford only a fairly limited number of fitness function evaluations in such cases. Despite these concerns, relatively few efforts have been reported in the literature to reduce the computational cost of MOEAs. It has been until relatively recently, that researchers have developed techniques to achieve an effective reduction of fitness function evaluations by exploiting knowledge acquired during the search. In this chapter, we analyze different proposals currently available in the specialized literature to deal with expensive functions in evolutionary multi-objective optimization. Additionally, we review some real-world applications of these methods, which can be seen as case studies in which such techniques led to a substantial reduction in the computational cost of the MOEA adopted. Finally, we also indicate some of the potential paths for future research in this area.

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Section: Conclusion and Future Research Pathsmentioning

confidence: 99%

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Santana‐Quintero

Montano

Coello

2010

Evolutionary Learning and Optimization

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“…The surrogate-based optimization (SBO) approach has been successfully used in the design problems using computationally expensive simulations 27,28 . With limited numerical simulations, the surrogates enable us to generate reliable approximation to the solution over a design space and assess the sensitivity and correlation of the various parameters.…”

Section: Figure 1 Dielectric-barrier Discharge Configurationmentioning

confidence: 99%

Surrogate Modeling for Characterizing the Performance of Dielectric Barrier Discharge Plasma Actuator

Cho

Jayaraman

Viana

et al. 2008

46th AIAA Aerospace Sciences Meeting and Exhibit

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The dielectric barrier discharge (DBD) plasma actuator offers promising opportunities for flow control because it does not require mass injection and involves no moving mechanical components. In order to gain better understanding of the impact of the materials and operational parameters on the performance of the DBD actuator, and to facilitate design of effective control schemes, the surrogate modeling technique is adopted. The model is established based on three design variables, namely (i) frequency of the applied voltage, (ii) dielectric constant of the insulator, and (iii) polarity (positive/negative) time ratio of the applied waveform, and focuses on two objectives, namely, (i) net force generated, and (ii) power requirement. The 2-species fluid plasma model with helium as a working gas is used in the computational model to generate the data needed by the surrogate model. Multiple surrogate models are compared to enhance the robustness of the surrogate performance. There exist multiple Pareto fronts where the x-directional force is positive with relatively low power and negative with high power respectively. Global sensitivity analysis indicates that the frequency of the applied voltage is important for the actuator performance in one region whereas the time ratio of the applied waveform is in the other, while the dielectric constant is always important. The performance dependency on variables also differs significantly according to the different regions.

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“…Formulations of the Sobol´s method that account for non-rectangular domains and correlated inputs are available; see, for example, Jacque et al (2004), and Mack et al (2005) for a recent application. A detailed discussion of global sensitivity methods and applications can be found in Saltelli and Tarantola (2002), Frey and Patil (2002) and the references therein.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis of Alkali–Surfactant–Polymer enhanced oil recovery processes

Carrero

Queipo

Pintos

et al. 2007

Journal of Petroleum Science and Engineering

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Multiple Surrogates for the Shape Optimization of Bluff Body-facilitated Mixing

Cited by 28 publications

References 10 publications

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

Surrogate Modeling for Characterizing the Performance of Dielectric Barrier Discharge Plasma Actuator

Global sensitivity analysis of Alkali–Surfactant–Polymer enhanced oil recovery processes

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