Variable-complexity response surface aerodynamic design of an HSCT wing

Giunta, Anthony A.; Narducci, Robert; Burgee, Susan L.; Grossman, Bernard; Mason, William H.; Watson, Layne T.; Haftka, Raphael T.

doi:10.2514/6.1995-1886

Cited by 29 publications

(26 citation statements)

References 5 publications

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“…One of the most important advantages obtained by using response surface models [15] in optimization is a significant reduction in the computational cost. This allows the user to perform global optimization and reliability-based optimization, which are otherwise prohibitively computationally expensive.…”

Section: Response Surface Modeling Methodsmentioning

confidence: 99%

Lift maximization with uncertainties for the optimization of high lift devices using Multi-Criterion Evolutionary Algorithms

Tang

Périaux

Bugeda

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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In this paper, the aerodynamic shape optimization problems with uncertain operating conditions has been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in Aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the Multi-Criterion Evolutionary Algorithms (MCEAs) are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier-Stokes computation using an unstructured mesh. The proposed approach drives to the solution of a multi-objective optimization problem that is solved using a modification of a Non-dominated Sorting Genetic Algorithm (NSGA). In order to reduce the number of expensive evaluations of the fitness function a Response Surface Modeling (RSM) is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem a Semi-torsional Spring Analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free-stream angle of attack at landing and takeoff flight conditions, respectively.

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Section: Response Surface Modeling Methodsmentioning

confidence: 99%

Lift maximization with uncertainties for the optimization of high lift devices using Multi-Criterion Evolutionary Algorithms

Tang

Périaux

Bugeda

et al. 2009

2009 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…One of the most important advantages obtained by using RSMs [15] in optimization is a significant reduction in the computational cost. This allows the user to perform global optimization and reliability-based optimization, which are otherwise prohibitively computationally expensive.…”

Section: Adaptive Response Surface Modeling (Arsm) Methodsmentioning

confidence: 99%

“…According to the Taguchi robust control theory, the above design problem with uncertainties can be converted into the following two-objective optimization problem, one objective is the mean value of the lift coefficient, and the other is the variance of lift coefficient over the range of uncertainty (27) where N = 5, M ∞ = 0.15, and i = [13,14,15,16,17 • ].…”

Section: Single-point Lift Maximization At Landing Flight Conditionmentioning

confidence: 99%

Lift maximization with uncertainties for the optimization of high‐lift devices

Tang

Périaux

Bugeda

et al. 2009

Numerical Methods in Fluids

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SUMMARYIn this paper, the aerodynamic shape optimization problems with uncertain operating conditions have been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the modified non-dominated sorting genetic algorithms are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier-Stokes computation using an unstructured mesh. In order to reduce the number of expensive evaluations of the fitness function a response surface modeling is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem, a semi-torsional spring analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high-lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free-stream angle of attack at landing flight condition.

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“…For aircraft structural design, previous decomposition schemes focused on the use of response surface approximations, fit to the values of multiple lower level optima, as a way to integrate the various design levels (e.g., Giunta et al, 1997b;Balabanov et al, 1999;Ragon et al, 2003;Liu et al, 2000). This involved performing hundreds of subsystem optimizations, and fitting the optimal response surface with a polynomial for use by the system optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Decomposition theory for multidisciplinary design optimization problems with mixed integer quasiseparable subsystems

Haftka

Watson

2006

Optim Eng

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Numerous hierarchical and nonhierarchical decomposition strategies for the optimization of large scale systems, comprised of interacting subsystems, have been proposed. With a few exceptions, all of these strategies are essentially heuristic in nature. Recent work considered a class of optimization problems, called quasiseparable, narrow enough for a rigorous decomposition theory, yet general enough to encompass many large scale engineering design problems. The subsystems for these problems involve local design variables and global system variables, but no variables from other subsystems. The objective function is a sum of a global system criterion and the subsystems' criteria. The essential idea is to give each subsystem a budget and global system variable values, and then ask the subsystems to independently maximize their constraint margins. Using these constraint margins, a system optimization then adjusts the values of the system variables and subsystem budgets. The subsystem margin problems are totally independent, always feasible, and could even be done asynchronously in a parallel computing context. An important detail is that the subsystem tasks, in practice, would be to construct response surface approximations to the constraint margin functions, and the system level optimization would use these margin surrogate functions. The present paper extends the quasiseparable necessary conditions for continuous variables to include discrete subsystem variables, although the continuous necessary and sufficient conditions do not extend to include integer variables.

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Variable-complexity response surface aerodynamic design of an HSCT wing

Cited by 29 publications

References 5 publications

Lift maximization with uncertainties for the optimization of high lift devices using Multi-Criterion Evolutionary Algorithms

Lift maximization with uncertainties for the optimization of high lift devices using Multi-Criterion Evolutionary Algorithms

Lift maximization with uncertainties for the optimization of high‐lift devices

Decomposition theory for multidisciplinary design optimization problems with mixed integer quasiseparable subsystems

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