Investigation of a continuous adjoint-based optimization procedure for aeroacoustic control of plane jets

Marinc, Daniel; Foysi, Holger

doi:10.1016/j.ijheatfluidflow.2012.07.005

Cited by 13 publications

(13 citation statements)

References 57 publications

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“…If we limit the time horizon, and just let the initial jet develop throughout the domain, then adjoint fields with reasonable magnitudes are obtained. This cumulative error growth while integrating backwards in time is also observed by Marinc and Foysi in the plane-jet optimization case [9], and similar problems for other test cases were reported in Refs. [40,41].…”

Section: Limitations Concerning the Adjoint-based Gradientsupporting

confidence: 87%

“…We employed distributed controls instead, which are designed as localized forcing regions that are distributed around the jet circumference, and are capable of introducing azimuthal perturbations. Similar adjoint-based jet control using distributed control was previously employed in noise reduction of round jets [11] and plane jets [9]. However, these studies employed local heat source regions in the domain.…”

Section: Cost Functional and Controlsmentioning

confidence: 99%

“…Early efforts using the adjoint method in combination with DNS or LES suffered due to this excessive storage requirement, and focused on simple canonical flows such as periodic channel flow [6], and spatially developing [7], or convective mixing layers [8]. More recently, optimal control using the adjoint method was applied to reduce jet-noise emisssions using the compressible Navier-Stokes equations, e.g., Marinc and Foysi [9], Schulze et al [10] and Kim et al [11].…”

Section: Introductionmentioning

confidence: 99%

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Optimal control of a transitional jet using a continuous adjoint method

Önder

Meyers

2016

Computers & Fluids

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The use of active flow control with unsteady means gains increasing interest in engineering designs. The main bottleneck of the methodology is the strong dependence on trial and error to find the right set of control parameters. In this context, adjoint-based control using high-fidelity simulations is a promising method to explore optimal values in large parameter spaces. However, the applicability of the methodology to complex engineering geometries remains extremely limited. In this work, we employ adjoint-based optimal control using unsteady high-fidelity simulations in a generic unstructured grid framework. To this end, an optimal flow control study is conducted in OpenFOAM R using the continuous adjoint method and DNS simulations. To demonstrate the methodology, we study control of an incompressible axisymmetric jet at Re D = 2000, with focus on improving its mixing properties. The gradient of the cost functional is calculated with a newly developed unsteady-adjoint solver based on a classical incremental projection scheme. Particular attention is paid into the presentation of mathematical and algorithmic details. Moreover, we address three main issues that remained relatively undiscussed in common practise: the choice of adjoint boundary conditions on computational boundaries, the failure of the adjoint methodology for long optimization horizons in turbulent flows and the treatment of the additional transposed convective term in the adjoint equations. Practical solutions are employed for these issues. Two optimization cases with different initial conditions are designed. To this end, we considered

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Section: Limitations Concerning the Adjoint-based Gradientsupporting

confidence: 87%

Section: Cost Functional and Controlsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal control of a transitional jet using a continuous adjoint method

Önder

Meyers

2016

Computers & Fluids

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“…To investigate the impact of the acoustic source reconstruction on the result computed by a hybrid aeroacoustic analysis a spanwise periodic isothermal plane jet at a ReynoldS number Re = 2000 based on the jet exit velocity u j and the jet height H has been analyzed using a large-eddy simulation (leS) [14] with a subsequent aeroacoustic simulation based on the acoustic perturbation equations (APe-4) [1]. The mACH number of the jet is 0.9.…”

Section: Application To Plane Jet Flowmentioning

confidence: 99%

Comparison of Source Reconstruction Methods for Hybrid Aeroacoustic Predictions

Geiser¹,

Marinc²,

Schröder³

2013

International Journal of Aeroacoustics

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Hybrid aeroacoustic prediction methods have gained increasing popularity to overcome the large differences between the fluid mechanics and acoustics length scales when noise generated by subsonic flows is investigated. The overall solution of those predictions essentially depends on the accurate representation of the acoustic sources leading to noise emissions. The sources are not generally known, since they are coupled to the turbulent dynamics of the fluid. A common approach is to determine the sources using computational fluid dynamics on a small domain of interest and to compute by an acoustic model the sound field on a larger domain. This coupling is susceptible since usually an interpolation in time is conducted to reduce the amount of data required for a proper source representation. The question of the impact of the interpolation on the acoustic solution arises or in other words, what interpolation formulation is necessary to not impact the acoustic result. Therefore, in this study, the quality of several time-interpolation methods is assessed to determine an appropriate acoustic source reconstruction. First, a general framework to design interpolating filters is presented that allows to obtain interpolation results of arbitrary order and differentiability and offers a configurable passband optimization. Several interpolators are evaluated in detail with special care for proper acoustic source reconstruction. The application to a subsonic turbulent plane jet at ReynoldS number Re = 2000 reveals a better source representation by constrained broadband optimized interpolation. least-squares optimized C 0 -interpolators with additional TAyloR constraints according to the order of the time integration method showed the best result. A broadband error reduction turned out to be more important for the source reconstruction when compared to a strict order of convergence. The TAyloR order of the interpolator, however, should be at least the TAyloR order of the temporal integrator to achieve the desired convergence of the overall solution scheme. HeRmiTian interpolation was found to be too error-prone to inaccuracies in the numerical approximation of the required temporal derivatives, even though having the theoretical benefit of C 1differentiability of the interpolation result.

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“…The application of optimal control theory to complex three-dimensional turbulence requires detailed numerical simulations to represent the desired effects of the controls and to capture the flow field response [1][2][3][4][5][6][7][8]. Examples cover different areas such as drag reduction in turbulent boundary layers [1,9], turbulent mixing [10,11], noise reduction [6,12] and wind-farm energy extraction [7,8,13].…”

Section: Introductionmentioning

confidence: 99%

Multigrid optimization for DNS-based optimal control in turbulent channel flows

Nita

Vandewalle

Meyers

2018

Journal of Computational Physics

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The use of PDE-constrained optimization techniques in combination with transient three-dimensional turbulent flow simulations such as Direct Numerical Simulation (DNS) or Large-Eddy Simulation (LES) involves large computational cost and memory resources. To date, the minimization of a DNS-based cost functional is typically achieved by applying classical single-grid gradient-based iterative methods of quasi-Newton or non-linear conjugate gradient type. In the current study, a multigrid optimization (MG/OPT) strategy is investigated in order to speed up gradient-based algorithms designed for large scale optimization problems. The method employs a hierarchy of optimization problems defined on different representation levels. It aims to reduce the computational resources associated with the cost functional improvement on the finest level. We apply the MG/OPT method in the context of direct numerical simulations of a fully developed channel flow problem. The performance of the multigrid optimization technique is compared against the single-grid optimization method in terms of equivalent function and gradient evaluations. Also the influence of the optimization problem properties and algorithmic parameters are investigated. It is found that, in some cases, the MG/OPT method accelerates the single-grid damped L-BFGS method by a factor of four.

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Investigation of a continuous adjoint-based optimization procedure for aeroacoustic control of plane jets

Cited by 13 publications

References 57 publications

Optimal control of a transitional jet using a continuous adjoint method

Optimal control of a transitional jet using a continuous adjoint method

Comparison of Source Reconstruction Methods for Hybrid Aeroacoustic Predictions

Multigrid optimization for DNS-based optimal control in turbulent channel flows

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