Multifidelity Optimization for High-Lift Airfoils

Demange, J; Savill, A. M.; Kipouros, Timoleon

doi:10.2514/6.2016-0557

Cited by 10 publications

(18 citation statements)

References 24 publications

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“…The method developed in this paper is an extension of the work carried by the authors in Ref. 26. Two approximations of the aerodynamic physics are used in order to speed up the convergence of the optimizer while retaining accuracy from the most accurate model.…”

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

“…In addition to the work presented in Ref. 26, the objective space dimension can be greater than 1: l ≥ 1. The goal is the minimization (or maximization) of the high-fidelity function as defined in (1), where x is a vector of dimension n and D is a subset of R n :…”

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

“…The trust region is usually defined as a ball centered on the initial point of the sub-problem but in Ref. 26, a second definition based on the number of improvements of the corrected model is used. This paper only uses the step-based trust region presented in more details in section II.C.…”

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

“…The single objective framework is presented in Ref. 26. The full diagram of its extension to multiobjective is shown in figure 1; the next sections follow the diagram and explain each part.…”

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

“…The results were encouraging with a reduction of 5 times of the number of high-fidelity calls. Finally, Demange et al 26 applied the derivative-free multifidelity method of Jarett et al to a two-dimensional multi-element airfoil. This paper is an extension of their work to multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

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A Multifidelity Multiobjective Optimization Framework for High-Lift Airfoils

Demange

Savill

Kipouros

2016

17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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High-lift devices design is a challenging task as it involves highly complex flow features while being critical for the overall performance of the aircraft. When part of an optimization loop, the computational cost of the Computational Fluid Dynamics becomes increasingly problematic. Methods to reduce the optimization time has been of major interest over the last 50 years. This paper presents a multiobjective multifidelity optimization framework that takes advantage of two approximation levels of the flow equations: a rapid method that provides quick estimates but of relatively low accuracy and a reference method that provides accurate estimations at the cost of a longer run-time. The method uses a suboptimization, under a trust-region scheme, performed on the low-fidelity model corrected by a surrogate model that is fed by the high-fidelity tool. The size of the trust region is changed according to the accuracy of the corrected model. The multiobjective optimizer is used to set the positions of the flap and slat of a two-dimensional geometry with lift and drag as objectives with an empirical-based method and a Reynolds Averaged Navier-Stokes equations solver. The multifidelity method shows potential for discovering the complete Pareto front, yet it remains less optimal than the Pareto front from the high-fidelity-only optimization.

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Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

Section: Multifidelity Optimization Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multifidelity Multiobjective Optimization Framework for High-Lift Airfoils

Demange

Savill

Kipouros

2016

17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis

Kontogiannis

Savill

2020

Optim Eng

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The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines is associated with a challenging design space. This provides a large pool of possible designs, requiring an efficient exploration scheme with the ability to provide sufficient feedback early in the design process. This paper demonstrates a generalized optimization framework with rapid design space exploration capabilities in which a Multifidelity approach is directly adjusted to the emerging needs of the design. The methodology is developed to be easily applicable and efficient in computationally expensive multidisciplinary problems. To accelerate such a demanding process, Surrogate Based Optimization methods in the form of both Radial Basis Function and Kriging models are employed. In particular, a modification of the standard Kriging approach to account for Multifidelity data inputs is proposed, aiming to increasing its accuracy without increasing its training cost. The surrogate optimization problem is solved by a Particle Swarm Optimization algorithm and two constraint handling methods are implemented. The surrogate model modifications are visually demonstrated in a 1D and 2D test case, while the Rosenbrock and Sellar functions are used to examine the scalability and adaptability behaviour of the method. Our particular Multiobjective formulation is demonstrated in the common RAE2822 airfoil design problem. In this paper, the framework assessment focuses on our infill sampling approach in terms of design and objective space exploration for a given computational cost.

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