Integrating User-Preference Swarm Algorithm and Surrogate Modeling for Airfoil Design

Carrese, Robert; Winarto, Hadi; Li, Xiaodong

doi:10.2514/6.2011-1246

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…However, this reduces the flexibility of finding another such region unless the weights are changed. Therefore, the Pareto method is used to find the best compromise of the performance parameters and typically, the results of the weighted sum method should lie somewhere on the Pareto front [12]. The advantage of using a Pareto method is that it allows the designer to be able to see the best compromise before making the decision.…”

Section: B Optimizationmentioning

confidence: 99%

Optimizing Rotor Blades with Approximate British Experimental Rotor Programme Tips

Johnson

Barakos

2014

Journal of Aircraft

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This work presents a framework for the optimization of certain aspects of a British Experimental Rotor Programme-like rotor blade in hover and forward flight so that maximum performance can be obtained from the blade. The proposed method employs a high-fidelity, efficient computational fluid dynamics technique that uses the harmonic balance method in conjunction with artificial neural networks as metamodels, and genetic algorithms for optimization. The approach has been previously demonstrated for the optimization of blade twist in hover and the optimization of rotor sections in forward flight, transonic aerofoils design, wing and rotor tip planforms. In this paper, a parameterization technique was devised for the British Experimental Rotor Programme-like rotor tip and its parameters were optimized for a forward flight case. A specific objective function was created using the initial computational fluid dynamics data and the metamodel was used for evaluating the objective function during the optimization using the genetic algorithms. The objective function was adapted to improve forward flight performance in terms of pitching moment and torque. The obtained results suggest optima in agreement with engineering intuition but provide precise information about the shape of the final lifting surface and its performance. The main computational cost was associated with the population of the genetic algorithms database necessary for the metamodel, especially because a full factorial method was used. The computational time of the optimization process itself, after the database has been obtained, is relatively insignificant. Therefore, the computational time was reduced with the use of the harmonic balance method as opposed to the time marching method. The novelty in this paper is twofold. Optimization methods so far have used simple aerodynamic models employing direct "calls" to the aerodynamic models within the optimization loops. Here, the optimization has been decoupled from the computational fluid dynamics data allowing the use of higher-fidelity computational fluid dynamics methods based on Navier-Stokes computational fluid dynamics. This allows a more realistic approach for more complex geometries such as the British Experimental Rotor Programme tip. In addition, the harmonic balance method has been used in the optimization process.

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Section: B Optimizationmentioning

confidence: 99%

Optimizing Rotor Blades with Approximate British Experimental Rotor Programme Tips

Johnson

Barakos

2014

Journal of Aircraft

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“…However, this reduces the flexibility of finding another such region unless the weights are changed. Therefore the Pareto method is used to find the best compromise of the performance parameters and typically, the results of the weighted sum method should lie somewhere on the Pareto front 11 . The advantages of using a Pareto method is that it allows the designer to be able to see the best compromise before making the decision.…”

Section: Introductionmentioning

confidence: 99%

Optimising Aspects of a BERP-like Rotor Using Frequency-Domain Methods

Johnson

Woodgate

Barakos

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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This work presents a framework for the optimisation of certain aspects of a BERPlike rotor blade in forward flight while constraining hover performance. The proposed method employs a high-fidelity, efficient CFD technique that uses the Harmonic Balance method in conjunction with artificial neural networks (ANNs) as metamodels, and genetic algorithms (GAs) for optimisation. The approach has been previously demonstrated for the optimisation of linear twist of rotors in hover (steady case) and the optimisation of rotor sections in forward flight (unsteady case), transonic aerofoils, wing and rotor tip planforms.In this paper, a parameterisation technique was defined for the BERP-like rotor tip and three of the parameters were optimised in forward flight. A specific objective function was created using the initial CFD data and the metamodel was used for evaluating the objective function during the optimisation using the GAs. The obtained results suggest optima in agreement with engineering intuition but provide precise information about the shape of the final lifting surface and its performance. The results were checked using different optimisation methods and were not sensitive to the employed techniques with substantial overlap between the outputs of the selected methods. The main CPU cost was associated with the population of the CFD database necessary for the metamodel using a full factorial method and even that was reduced with the use of the Harmonic Balance method.

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“…Of particular significance is the ability to converge over large multimodal design spaces (which are typical of engineering design problems) and precision in the exploitation of individual solutions [14,15]. In this paper, we aim to progress further with the concept of userpreference optimization, by exploring the concept of surrogateassisted optimization (SAO).…”

mentioning

confidence: 99%

“…A reference point, which is specified as an array of m aspiration values, is projected onto the Pareto landscape by the designer to guide the swarm toward solutions of interest. Unlike goal attainment methods, which make explicit reference to a target design [13], the reference point is a means of expressing the designer's preferred level of compromise, which can ideally be based on an existing or target design [14][15][16]. The swarm is guided by this information to confine its search to the preferred region of the Pareto front in the vicinity of the reference point.…”

mentioning

confidence: 99%

Swarm Heuristic for Identifying Preferred Solutions in Surrogate-Based Multi-Objective Engineering Design

Carrese¹,

Sóbester

Winarto³

et al. 2011

AIAA Journal

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Exploring the entire Pareto frontier of high-fidelity multidisciplinary problems can be prohibitive due to the excessive number of expensive evaluations required. The use of surrogate models offers promise toward managing such problems, which are restricted by a computational budget. In this paper, the kriging-assisted user-preference multi-objective particle swarm heuristic is presented, in which less accurate but inexpensive surrogate models are used cooperatively with the precise but expensive objective functions to alleviate the computational burden. A userpreference module is integrated into the optimization framework, which guides the swarm toward preferred regions of the Pareto frontier, thereby focusing all computing effort on identifying only solutions of interest to the designer. While providing a logical criterion to prescreen candidates for precise evaluation, the additional guidance provided by user-preferences guarantees an accelerated convergence rate. To depict the proficiency of the proposed framework, a suite of test problems, including the multidisciplinary cross-sectional design of a semimonocoque fuselage enclosing a pressurized cabin and payload bay, is presented. A parametric model is described that is capable of generating a broad range of double-lobe fuselage designs. The superiority of the kriging-assisted user-preference multi-objective particle swarm optimization algorithm over more traditional search methods to efficiently manage high-fidelity discontinuous design problems is highlighted.

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Integrating User-Preference Swarm Algorithm and Surrogate Modeling for Airfoil Design

Cited by 9 publications

References 26 publications

Optimizing Rotor Blades with Approximate British Experimental Rotor Programme Tips

Optimizing Rotor Blades with Approximate British Experimental Rotor Programme Tips

Optimising Aspects of a BERP-like Rotor Using Frequency-Domain Methods

Swarm Heuristic for Identifying Preferred Solutions in Surrogate-Based Multi-Objective Engineering Design

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