Application of a Weighted Average Surrogate Approach to Helicopter Rotor Blade Vibration Reduction

Glaz, Bryan; Goel, Tushar; Liu, Li; Friedmann, Peretz P.; Haftka, Raphael T.

doi:10.2514/6.2007-1898

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…First, for the kth sub-domain, construct multiple different candidate surrogates, and calculate the corresponding GMSE based on leave-one-out cross-validation by (15) of each surrogate as the criterion to measure its accuracy. Then the sequence of the surrogates according to GMSE in the ascending order can be obtained, and denote the candidate model set with the ranking sequence as M k so as to compose a more accurate ensemble, the first issue is to define the number of contributing surrogates n k * M ≤ n M to be selected.…”

Section: Surrogate Ensemble Modeling For Sctv Predictionmentioning

confidence: 99%

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An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Zhang

Yao³

et al. 2019

Applied Sciences

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The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.

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Section: Surrogate Ensemble Modeling For Sctv Predictionmentioning

confidence: 99%

“…One popular ensemble method is using the weighted sum approach [14]. Goel et al [15] study the effectiveness of the weighted aggregation method for the approximation of helicopter vibrations. Wang et al [16] employ the weighted average surrogate to solve the problem of computationally expensive function evaluations in optimization.…”

Section: Introductionmentioning

confidence: 99%

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Zhang

Yao³

et al. 2019

Applied Sciences

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“…They showed that due to small islands in the design space where mixing is very effective compared to the rest of the design space, it is difficult to use a single surrogate model to capture such local but critical features. Glaz et al 135 used polynomial response surfaces, kriging, radial basis neural networks, and weighted average surrogate for helicopter rotor blade vibration reduction. Their results indicated that multiple surrogates can be used to locate low vibration designs which would be overlooked if only a single approximation method was employed.…”

Section: Multiple Surrogates and Metamodel Ensemblesmentioning

confidence: 99%

Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not

Simpson

Toropov

Balabanov

et al. 2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

243

148

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The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past two decades, but how far have we really come? This is the question that we investigate in this paper, namely, the extent to which the use of metamodeling techniques in multidisciplinary design optimization have evolved in the two decades since the seminal paper on Design and Analysis of Computer Experiments by Sacks et al. As part of this review, we examine the motivation for advancements in metamodeling techniques from both a historical perspective and the research itself. Based on current thrusts in the field, we emphasize multi-level/multi-fidelity approximations and ensembles of metamodels, as well as the availability of metamodels within commercial software and for design space exploration and visualization in this review. Our closing remarks offer insight into future research directions-nearly the same ones that have motivated us in the past. I.

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“…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. This combined approach, adopted a weighted estimation from the different models, which was used to reduce the vibration for a helicopter rotor blade.…”

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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Application of a Weighted Average Surrogate Approach to Helicopter Rotor Blade Vibration Reduction

Cited by 12 publications

References 39 publications

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not

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

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