Morembs—A Model Order Reduction Package for Elastic Multibody Systems and Beyond

Fehr, Jörg; Grunert, Dennis; Holzwarth, Philip; Fröhlich, Benjamin; Walker, Nadine; Eberhard, Peter

doi:10.1007/978-3-319-75319-5_7

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…In the following, different basis generation approaches are presented with the goal to keep the approximation error as small as possible. The bases are generated by using the extensive software packages MatMorembs 3 [20] and RBmatlab 4 , cf. 8 and 9 .…”

Section: Model Order Reduction -Basis Generationmentioning

confidence: 99%

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar

Rettberg¹,

Wittwar²,

Buchfink³

et al. 2022

Preprint

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A fluid-structure interaction model in a port-Hamiltonian representation is derived for a classical guitar. We combine the laws of continuum mechanics for solids and fluids within a unified port-Hamiltonian (pH) modeling approach by adapting the discretized equations on second-order level in order to obtain a damped multi-physics model. The high-dimensionality of the resulting system is reduced by model order reduction. The article focuses on pH-systems in different state transformations, a variety of basis generation techniques as well as structurepreserving model order reduction approaches that are independent from the projection basis. As main contribution a thorough comparison of these method combinations is conducted. In contrast to typical frequency-based simulations in acoustics, transient time simulations of the system are presented. The approach is embedded into a straightforward workflow of sophisticated commercial software modeling and flexible in-house software for multi-physics coupling and model order reduction.

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Section: Model Order Reduction -Basis Generationmentioning

confidence: 99%

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar

Rettberg¹,

Wittwar²,

Buchfink³

et al. 2022

Preprint

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“…This was reached by matching the transfer function of 4 inputs and outputs distributed over the plate at 20 frequency shifts equally distributed in the frequency range and 60 parameter expansion points created with a Sobol sequence as explained in 6 . General information on model order reduction can be found in 43,44 while a review of parametric model order reduction techniques is given in 45 , and the used software is described in 46 . The order-reduction approach reduces the computational time to calculate the first 30 modal parameters from 78 s with the full-order model to 0.04 s with the reduced-order model, corresponding to a numerical speedup of 1950.…”

Section: Materials Parameter Identificationmentioning

confidence: 99%

Model-predicted geometry variations to compensate material variability in the design of classical guitars

Brauchler

González

Vierneisel

et al. 2022

Preprint

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Musical instrument making is often considered a mysterious form of art, its secrets still escaping scientific quantification. There is not yet a formula to make a good instrument, so historical examples are regarded as the pinnacle of the craft. This is the case of Stradivari’s violins or Torres guitars that serve as both models and examples to follow. Geometric copies of these instruments are still the preferred way of building new ones, yet reliably making acoustic copies of them remains elusive. One reason for this is that the variability of the wood used for instruments makes for a significant source of uncertainty -- no two pieces of wood are the same. In this article, using state-of-the-art methodologies, we show a method for matching the vibrational response of two guitar top plates made with slightly different materials. To validate our method, we build two guitar soundboards: one serving as a reference and the second acting as a copy to which we apply model-predicted geometry variations. The results are twofold. Firstly, we can experimentally validate the predictive capabilities of our numerical model regarding geometry changes. Secondly, we can significantly reduce the deviation between the two plates by these precisely predicted geometry variations. Although applied to guitars here, the methodology can be extended to other instruments, e.g. violins, in a similar fashion. The implications of such a methodology for the craft could be far-reaching by turning instrument-making more into a science than artistic craftsmanship and paving the way to accurately copy historical instruments of a high value.

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“…Our Gramian computation methods are integrated in a range of MOR software packages, though. While sssMOR [19] directly calls M-M.E.S.S.-1.0.1, for other packages like MOREMBS [22] and MORPACK [40] we have contributed tailored versions of our algorithms. Also, we provide tools like a square root method (SRM) function to compute the transformation matrices V and W from given Gramian factors.…”

Section: Model Order Reduction In M-messmentioning

confidence: 99%

Matrix Equations, Sparse Solvers: M-M.E.S.S.-2.0.1—Philosophy, Features, and Application for (Parametric) Model Order Reduction

Benner

Köhler

Saak

2021

Model Reduction of Complex Dynamical Systems

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Matrix equations are omnipresent in (numerical) linear algebra and systems theory. Especially in model order reduction (MOR) they play a key role in many balancing based reduction methods for linear dynamical systems. When these systems arise from spatial discretizations of evolutionary partial differential equations, their coefficient matrices are typically becoming large and sparse. Moreover, the numbers of inputs and outputs of these systems are typically far smaller than the number of spatial degrees of freedom. Then, in many situations the solutions of the corresponding large-scale matrix equations are observed to have low (numerical) rank. This feature is exploited by M-M.E.S.S. to find successively larger low-rank factorizations approximating the solutions. This contribution describes the basic philosophy behind the implementation and the features of the package, as well as its application in the model order reduction of large-scale linear time-invariant (LTI) systems and parametric LTI systems.

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Morembs—A Model Order Reduction Package for Elastic Multibody Systems and Beyond

Cited by 12 publications

References 25 publications

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar

Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar

Model-predicted geometry variations to compensate material variability in the design of classical guitars

Matrix Equations, Sparse Solvers: M-M.E.S.S.-2.0.1—Philosophy, Features, and Application for (Parametric) Model Order Reduction

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