A greedy reduced basis algorithm for structural acoustic systems with parameter and implicit frequency dependence

Jelich, Christopher; Baydoun, Suhaib Koji; Voigt, Matthias; Marburg, Steffen

doi:10.1002/nme.6835

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…Recent research, thus, focuses on acceleration techniques to improve the efficiency in frequency sweep analyses. For instance, a greedy reduced basis [1][2][3] or parametric model order reduction methods [4][5][6][7] have been developed. The interested reader is referred to [8] for a thorough review in this area.…”

Section: Introductionmentioning

confidence: 99%

Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems

Gurbuz,

Eser,

Marburg

2023

Proc Appl Math and Mech

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This paper presents a multi‐fidelity model for the acceleration of frequency sweep analyses in acoustics. In traditional analyses, the frequency‐dependent Helmholtz equation is repetitively solved at each frequency. Using the boundary element method requires then a plethora of evaluations resulting in high computational costs. In the proposed method, the fidelity levels are realized as Gaussian processes, which are conditioned on observations obtained by boundary element simulations. A coarse boundary element mesh is considered as the low‐fidelity model, whereas a fine mesh is adopted as the high‐fidelity model. To validate the proposed framework, the vehicle interior noise problem is investigated. The results demonstrate that multi‐fidelity Gaussian processes efficiently accelerate the frequency sweep analysis, as they provide accurate and fast predictions across the entire frequency range of interest. As a beneficial side effect, the present method takes uncertainties into account. This allows to consider limited information on the model, which is particularly important in early design phases.

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Section: Introductionmentioning

confidence: 99%

Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems

Gurbuz,

Eser,

Marburg

2023

Proc Appl Math and Mech

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“…20 Moving towards the direction of reducing the sampling in the parameter domain, an approach that avoids a-priori sampling of the parametric domain has been proposed, 17,21 which treats low rank parametric changes as extra inputs and constructs the reduction basis by a Krylov moment matching strategy. Finally, a reduced basis approach has been recently proposed, 22 which however requires several full BEM systems to be stored simultaneously and can therefore result into a memory shortage.…”

Section: Introductionmentioning

confidence: 99%

Parametric model order reduction for acoustic boundary element method systems through a multiparameter Krylov subspaces recycling strategy

Panagiotopoulos

Desmet

Deckers

2022

Numerical Meth Engineering

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The recently introduced framework enabling model order reduction (MOR) for systems arising within the acoustic boundary element method (BEM), allows for fast frequency sweeps to obtain the detailed response of an acoustic system. Nevertheless, in many cases it is desirable to include additional parameters in the MOR scheme to facilitate iterations among different configurations of a specific design. In that context, this work proposes a multiparameter Krylov subspaces recycling scheme, based on the algorithms proposed in Reference 23 for a single parameter, aiming at the parametric MOR of acoustic BEM systems. The proposed scheme allows to include multiple parameters along with the non‐affine wavenumber kernel dependency and thus facilitates the fast solution of parametric acoustic systems. The article distinguishes among three different parametrizations of BEM systems: (i) a right‐hand side parametrization, (ii) an affine, and (iii) a non‐affine parametrization of the system matrix. The two former cases allow for the construction of a cheap residual error estimator and thus facilitate the deployment of a greedy sampling strategy. This work proposes the employment of such a greedy strategy in the context of Krylov subspaces recycling, which combined with the automatic Krylov subspaces recycling (AKR) algorithm, enables the construction of a global reduction basis incorporating such parametric variations. On the contrary, since in case of non‐affine parametrizations a cheap residual error estimator is not directly available, a multiparameter AKR algorithm is deployed to ensure the predefined residual level. The performance of the proposed methods is benchmarked against a reduced basis method‐type approach, which follows the same assumptions as the AKR for the case of non‐affine systems. The potential of the algorithms is demonstrated on academic BEM examples including source position, material, and shape parametrizations and significant reductions in the sampling required for the construction of the respective reduction bases are reported.

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Numerical and Experimental Assessment of Acoustic Radiation Damping

Baydoun,

Marburg

2023

Lecture Notes in Applied and Computational Mechanics

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A greedy reduced basis algorithm for structural acoustic systems with parameter and implicit frequency dependence

Cited by 14 publications

References 38 publications

Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems

Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems

Parametric model order reduction for acoustic boundary element method systems through a multiparameter Krylov subspaces recycling strategy

Numerical and Experimental Assessment of Acoustic Radiation Damping

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