arXiv subjects

Linus Taenzer

Publications and source records attributed to Linus Taenzer.

3 recordsLinked to original sources

arXiv2026

A scalability benchmark study of model order reduction techniques for very large, strongly coupled vibroacoustic problems

Model Order Reduction (MOR) can significantly reduce the computational cost of vibroacoustic simulations. While most MOR research focuses on single-domain systems (e.g., structural dynamics or computational fluid mechanics), this work compares MOR techniques for large multi-domain problems to identify methods that remain efficient and accurate at very large scales. In particular, harmonic response simulations of vibroacoustic fluid-structure coupled systems used to compute transfer functions from an input force to either structural acceleration or pressure in the heavy fluid domain are of high interest. To achieve this, the most common MOR techniques based on modal methods and Krylov subspace methods are compared for multi-material systems. To assess the feasibility and accuracy of these techniques for different system sizes, a scalable benchmark model of a water-filled Plexiglass cylinder is developed, with mesh sizes from 10,000 to 1,000,000 Degrees of Freedom (DOF). The quality of the models is assured by validation against experimental data. The geometry, model data, and experimental results are made available so that they can be used as a benchmark for further studies. For systems larger than 100,000 DOF, the investigated modal methods become impractical due to memory limitations, even on powerful workstations. Among the tested techniques, a Krylov subspace two-level orthogonal Arnoldi reduction, combined with symmetrization and conditioning of the system matrices, provides the most accurate and efficient approximation of the target transfer functions - particularly for large-scale models up to 1,000,000 DOF. This approach achieves a speedup of up to 600 times compared to the full model.

physics.app-ph↗

arXiv2025

Matching frequency response measurements and reduced order models for the inverse identification of viscoelastic properties

3D-printed materials are used in many different industries (automotive, aviation, medicine, etc.). Most of these 3D-printed materials are based on ceramics or polymers whose mechanical properties vary with frequency. For numerical modeling, it is crucial to characterize this frequency dependency accurately to enable realistic finite-element simulations. At the same time, the damping behavior plays a key role in product development, since it governs a component's response at resonance and thus impacts both performance and longevity. In current research, inverse material characterization methods are getting more and more popular. However, their practical validation and applicability on real measurement data have not yet been discussed widely. In this work, we show the identification of two different materials, POM and additively manufactured sintered ceramics, and validate it with experimental data of a well-established measurement technique (dynamic mechanical analysis). The material identification process considers state-of-the-art reduced-order modeling and constrained particle swarm optimization, which are used to fit the frequency response functions of point measurements obtained by a laser Doppler vibrometer. This work shows the quality of the method in identifying the parameters defining the viscoelastic fractional derivative model, including their uncertainty. It also illustrates the applicability of this identification method in the presence of practical difficulties that come along with experimental data such as boundary conditions and noise.

physics.app-ph↗

arXiv2023

Generative inverse design of multimodal resonant structures for locally resonant metamaterials

In the development of locally resonant metamaterials, the physical resonator design is often omitted and replaced by an idealized mass-spring system. This paper presents a novel approach for designing multimodal resonant structures, which give rise to multi-bandgap metamaterials with predefined band gaps. Our method uses a conditional variational autoencoder to identify nontrivial patterns between design variables of complex-shaped resonators and their modal effective parameters. After training, the cost of generating designs satisfying arbitrary criteria - frequency and mass of multiple modes - becomes negligible. An example of a resonator family with six geometric variables and two targeted modes is further elaborated. We find that the autoencoder performs well even when trained with a limited dataset, resulting from a few hundred numerical modal analyses. The method generates several designs that very closely approximate the desired modal characteristics. The accuracy of the best designs, proposed by the auto-encoder, is confirmed in tests of 3D-printed resonator prototypes. Further experiments demonstrate the close agreement between the measured and desired dispersion relation of a sample metamaterial beam.

physics.app-ph↗