Optimal Design of Active Flow Control for a Complex High-Lift Configuration

Nemili, Anil; Özkaya, Emre; Gauger, Nicolas R.; Krämer, Felix; Hoell, Tobias; Thiele, Frank

doi:10.2514/6.2014-2515

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…Furthermore, the adjoint solver inherits the robustness and the asymptotic rate of convergence of the underlying URANS solver [17]. The ability of the AD based approach in accurate computation of sensitivities in incompressible URANS simulations is successfully demonstrated on practical configurations [26].…”

Section: A Discrete Adjoint Approach For Optimal Active Flow Controlmentioning

confidence: 99%

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A Two-Level Hybrid Approach for Optimal Active Flow Control on a Three-Element Airfoil

Nemili¹,

Özkaya²,

Gauger³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. In this paper we present a two-level approach that combines an adjoint-based gradient search method with an evolutionary algorithm for optimal active flow control. The suggested method effectively combines the advantages of both approaches and achieves a good compromise between the computational effort and the degree of freedom used in optimization. In the first level, a global optimization is performed with few design parameters using an evolutionary algorithm. In the second level, the global optimal solution from the first level is taken as the initial setting for the adjoint based local optimization using a large number of design parameters. The unsteady discrete adjoint solver required for the second level is developed based on Algorithmic Differentiation techniques for the unsteady incompressible flowsgoverned by Unsteady Reynolds-Averaged Navier Stokes (URANS) equations. In this way, the discrete adjoint solver is robust and has exactly the same functionality with the underlying URANS flow solver. The applicability of the two-level method is demonstrated by finding the optimal parameters of the active flow control mechanism on a three element airfoil configuration at a Reynolds number of Re = 10 6 and an angle of attack of AoA = 6 • . Numerical results have shown that the hybrid approach completely suppressed the separation and very significantly increased the mean-lift coefficient by 67% compared to the un-actuated baseline flow.

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Section: A Discrete Adjoint Approach For Optimal Active Flow Controlmentioning

confidence: 99%

“…The discrete adjoint URANS code ELAN-A [16,26] is developed by applying the AD tool Tapenade [27] to the underlying URANS solver. ELAN-A retains all features of the ELAN code.…”

Section: Numerical Solversmentioning

confidence: 99%

A Two-Level Hybrid Approach for Optimal Active Flow Control on a Three-Element Airfoil

Nemili¹,

Özkaya²,

Gauger³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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“…This is an extremely attractive characteristic for unsteady optimization problems in the multidisciplinary setting using a suite of multi-physics solvers where the objective function may be different depending on the type of problems being addressed. AD-based discrete adjoint has been applied to steady aerodynamic shape design problems by Hovland et al 17 and Gauger et al 18 More recently, it was successfully applied by Nemeli et al 19 in the unsteady setting to the active flow control of an industryrelevant high-lift configuration HIREX from Airbus.…”

Section: Introductionmentioning

confidence: 99%

Towards Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation

Zhou

Gauger

Yao

et al. 2019

AIAA Scitech 2019 Forum

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In this paper, we present an adjoint-based broadband noise minimization framework using stochastic noise generation (SNG). The SNG module is implemented in the open-source multi-physics solver suite SU2 and coupled with the existing Reynolds-averaged Navier-Stokes (RANS) to allow fast assessment of broadband noise sources. In addition, a discrete adjoint solver on the basis of algorithmic differentiation (AD) is developed for the coupled RANS-SNG system to enable efficient evaluation of broadband noise design sensitivities. The adjoint-based RANS-SNG framework developed in this work not only avoids the regularization problem that plagues the adjoint solutions for scale-resolving simulations, but also significantly lowers the computational cost and leads to a faster turnaround time for the initial design evaluation phase. Current results show that the RANS-SNG method can efficiently provide broadband noise level assessment for various configurations without resorting to computationally prohibitive scale-resolving simulations. Furthermore, current results also show that the AD-based coupled adjoint-RANS-SNG solver is highly accurate. Finally, shape optimizations performed on the basis of such coupled-sensitivity are shown to be effective in removing the broadband noise source in the trailing edge of a NACA0012 airfoil profile while maintaining aerodynamic performance imposed as an optimization constraint.

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“…Applications in aerodynamics include e.g. optimal active flow control, optimal shape design, inverse design or noise reduction [1,10,28,29,36,42].…”

Section: Introductionmentioning

confidence: 99%

A non-intrusive parallel-in-time approach for simultaneous optimization with unsteady PDEs

Günther

Gauger

Schroder

2018

Optimization Methods and Software

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This paper presents a non-intrusive framework for integrating existing unsteady partial differential equation (PDE) solvers into a parallel-in-time simultaneous optimization algorithm. The time-parallelization is provided by the non-intrusive software library XBraid [41], which applies an iterative multigrid reduction technique to the time domain of existing timemarching schemes for solving unsteady PDEs. Its general user-interface has been extended in [19] for computing adjoint sensitivities such that gradients of output quantities with respect to design changes can be computed parallel-in-time alongside with the primal PDE solution. In this paper, the primal and adjoint XBraid iterations are embedded into a simultaneous optimization framework, namely the One-shot method. In this method, design updates towards optimality are employed after each state and adjoint update such that optimality and feasibility of the design and the PDE solution are reached simultaneously. The time-parallel optimization method is validated on an advection-dominated flow control problem which shows significant speedup over a classical time-serial optimization algorithm.

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Optimal Design of Active Flow Control for a Complex High-Lift Configuration

Cited by 13 publications

References 5 publications

A Two-Level Hybrid Approach for Optimal Active Flow Control on a Three-Element Airfoil

A Two-Level Hybrid Approach for Optimal Active Flow Control on a Three-Element Airfoil

Towards Adjoint-based Broadband Noise Minimization using Stochastic Noise Generation

A non-intrusive parallel-in-time approach for simultaneous optimization with unsteady PDEs

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