Publications
182 results found
Wedler M, Stender M, Klein M, et al., 2022, Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data., Neural Netw, Vol: 156, Pages: 123-134
Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, increasing weight initialization robustness, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto-Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.
He Y, Ducrozet G, Hoffmann N, et al., 2022, Galilean-transformed solitons and supercontinuum generation in dispersive media, PHYSICA D-NONLINEAR PHENOMENA, Vol: 439, ISSN: 0167-2789
He Y, Witt A, Trillo S, et al., 2022, Extreme wave excitation from localized phase-shift perturbations, PHYSICAL REVIEW E, Vol: 106, ISSN: 2470-0045
Yang Z, Pan J, Chen J, et al., 2022, A novel unknown-input and single-output approach to extract vibration patterns via a roving continuous random excitation., ISA Trans, Vol: 129, Pages: 675-686
Operating deflection shape analysis allows investigating the dynamic behaviour of a structure during operation. It normally requires simultaneous, multi-point measurements to capture the response from an unknown excitation source (unknown-input and multiple-output), which can complicate its usage for structures without ease of access. A novel vibration pattern testing method is proposed based on a roving continuous random excitation employing a small robotic Hexbug device and a single-point measurement. The Hexbug introduces a random excitation in consecutive locations while roaming over the structure. The resulting multi-modal, time and location dependent response of the system is captured in a single location, and then analysed with a newly developed method based on empirical wavelet transform, multiscale morphological filtering and optimization to extract the excited vibration patterns. The efficiency of the proposed method is experimentally demonstrated on a free-free and a cantilevered beam with comparison to mode shapes extracted by hammer test. The validation highlights its ability to extract several vibration patterns from a long slender structure with good accuracy and robustness, with the general ability to expand the usability of an operating deflecting shape analysis.
Deutzer M, Stender M, Tüpker N, et al., 2022, A Novel Approach for the Frequency Shift of a Single Component Eigenmode through Mass Addition in the Context of Brake Squeal Reduction, SAE International Journal of Passenger Vehicle Systems, Vol: 16, ISSN: 2770-3460
Brake squeal reduces comfort for the vehicle occupants, damages the reputation of the respective manufacturer, and can lead to financial losses due to cost-intensive repair measures. Mode coupling is mainly held responsible for brake squeal today. Two adjacent eigenfrequencies converge and coalesce due to a changing bifurcation parameter. Several approaches have been developed to suppress brake squeal through structural changes. The main objective is to increase the distance of coupling eigenfrequencies. This work proposes a novel approach to structural modifications and sizing optimization aiming for a start at shifting a single component eigenfrequency. Locations suitable for structural changes are derived such that surrounding modes do not significantly change under the modifications. The positions of modifications are determined through a novel sensitivity calculation of the eigenmode to be shifted in frequency. In the present work, the structural changes are carried out on a beam and a brake caliper. Selected eigenfrequencies are shifted while the frequencies of the other eigenmodes are simultaneously fixed. Experimental investigations for the brake caliper validate the numerical findings and the applicability as well as efficiency of the proposed methods.
Stender M, Tiedemann M, Hoffmann N, 2022, Energy harvesting below the onset of flutter (vol 458, pg 17, 2019), JOURNAL OF SOUND AND VIBRATION, Vol: 534, ISSN: 0022-460X
Hartmann MCN, Onorato M, De Vita F, et al., 2022, Hydroelastic potential flow solver suited for nonlinear wave dynamics in ice-covered waters, OCEAN ENGINEERING, Vol: 259, ISSN: 0029-8018
Luenser H, Hartmann M, Desmars N, et al., 2022, The Influence of Characteristic Sea State Parameters on the Accuracy of Irregular Wave Field Simulations of Different Complexity, FLUIDS, Vol: 7
Klein M, Stender M, Wedler M, et al., 2022, APPLICATION OF MACHINE LEARNING FOR THE GENERATION OF TAILORED WAVE SEQUENCES
This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective wave sequence at the wave board. The synthetic training and validation data were acquired by applying the high-order spectral (HOS) method. The HOS method is a very accurate method for modeling non-linear wave propagation and its numerical efficiency allows the generation of large synthetic data sets. The training data featured wave groups of short duration based on JONSWAP spectra. The sea state parameters wave steepness, wave period and enhancement factor were systematically varied. At the end of the training process, the trained models were able to predict the wave sequences at the wave board based on the time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models were evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models was compared with the classical linear transformation approach.
Desmars N, Hartmann M, Behrendt J, et al., 2022, NONLINEAR RECONSTRUCTION AND PREDICTION OF REGULAR WAVES
A method for the reconstruction of nonlinear ocean surfaces is presented and applied to regular waves. From random samples of surface elevation, the method reconstructs the nonlinear features of the observed waves by means of the High-Order Spectral approach. The reconstructed surface is then propagated to provide a prediction at a later time. The agreement of the reconstructed and predicted surfaces with the reference one is quantified for a wide range of wave steepness. In each case, the accuracy of the surface elevation and surface velocity potential is evaluated for the first-, second- and third-orders of nonlinearity, while the reference surface corresponds to a fourth-order solution. This way, the improvement of the solution pertaining to each order of nonlinearity can be easily identified. The results show that the grid-based method is able to correctly reconstruct highly nonlinear regular waves, providing an accurate initial solution for the surface propagation. Due to the effect of the nonlinear dispersion, it is further shown that the third order of nonlinearity is necessary to obtain an accurate reconstruction/prediction of steep waves.
Kimmoun O, Hsu H-C, Hoffmann N, et al., 2021, Experiments on uni-directional and nonlinear wave group shoaling, OCEAN DYNAMICS, Vol: 71, Pages: 1105-1112, ISSN: 1616-7341
Stender M, Hoffmann N, 2021, bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems, NONLINEAR DYNAMICS, Vol: 107, Pages: 1451-1468, ISSN: 0924-090X
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- Citations: 5
Chabchoub A, Slunyaev A, Hoffmann N, et al., 2021, The Peregrine Breather on the Zero-Background Limit as the Two-Soliton Degenerate Solution: An Experimental Study, FRONTIERS IN PHYSICS, Vol: 9, ISSN: 2296-424X
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- Citations: 4
Neidhardt M, Ohlsen J, Hoffmann N, et al., 2021, Parameter Identification for Ultrasound Shear Wave Elastography Simulation, Current Directions in Biomedical Engineering, Vol: 7
Elasticity of soft tissue is a valuable information to physicians in treatment and diagnosis of diseases. The elastic properties of tissue can be estimated with ultrasound (US) shear wave imaging (SWEI). In US-SWEI, a force push is applied inside the tissue and the resulting shear wave is detected by high-frequency imaging. The properties of the wave such as the shear wave velocity can be mapped to tissue elasticity. Commonly, wave features are extracted by tracking the peak of the shear wave, estimating the phase velocity or with machine learning methods. To tune and test these methods, often simulation data is employed since material properties and excitation can be accurately controlled. Subsequent validation on real US-SWEI data is in many cases performed on tissue phantoms such as gelatine. Clearly, validation performance of these procedures is dependent on the accuracy of the simulated tissue phantom and a thorough comparison of simulation and experimental data is needed. In this work, we estimate wave parameters from 400 US-SWEI data sets acquired in various homogeneous gelatine phantoms. We tune a linear material model to these parameters. We report an absolute percentage error for the shear wave velocity between simulation and phantom experiment of <2.5%. We validate our material model on unknown gelatine concentrations and estimate the shear wave velocity with an error <3.4% for in-range concentrations indicating that our material model is in good agreement with US-SWEI measurements.
Stender M, Wedler M, Hoffmann N, et al., 2021, Explainable machine learning: A case study on impedance tube measurements, INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Vol: 263, Pages: 3223-3234, ISSN: 0736-2935
<jats:p>Machine learning (ML) techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepening the understanding of the decision-making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficient measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e. how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.</jats:p>
Klein M, Clauss GF, Hoffmann N, 2021, Introducing envelope soliton solutions for wave-structure investigations, OCEAN ENGINEERING, Vol: 234, ISSN: 0029-8018
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- Citations: 1
Papangelo A, Putignano C, Hoffmann N, 2021, Critical thresholds for mode-coupling instability in viscoelastic sliding contacts, NONLINEAR DYNAMICS, Vol: 104, Pages: 2995-3011, ISSN: 0924-090X
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- Citations: 3
Niedergesass B, Papangelo A, Grolet A, et al., 2021, Experimental observations of nonlinear vibration localization in a cyclic chain of weakly coupled nonlinear oscillators, Journal of Sound and Vibration, Vol: 497, Pages: 1-10, ISSN: 0022-460X
Experimental results on nonlinear vibration localization in a cyclic chain of weakly coupled oscillators with clearance nonlinearity are reported. Numerical modelling and analysis complements the experimental study. A reduced order model is derived and numerical analysis based on the harmonic balance method demonstrates the existence of multiple classes of stable spatially localized nonlinear vibration states. The experiments agree very well with the numerical results. The findings suggest that vibration localization due to fundamentally nonlinear effects may also arise in mechanical structures with relevance in engineering.
Fontanela F, Vizzaccaro A, Auvray J, et al., 2021, Nonlinear vibration localisation in a symmetric system of two coupled beams, Nonlinear Dynamics, Vol: 103, Pages: 3417-3428, ISSN: 0924-090X
We report nonlinear vibration localisation in a system of two symmetric weakly coupled nonlinear oscillators. A two degree-of-freedom model with piecewise linear stiffness shows bifurcations to localised solutions. An experimental investigation employing two weakly coupled beams touching against stoppers for large vibration amplitudes confirms the nonlinear localisation.
Stender M, Adams C, Wedler M, et al., 2021, Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tubea), JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, Vol: 149, Pages: 1932-1945, ISSN: 0001-4966
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- Citations: 5
Stender M, Tiedemann M, Spieler D, et al., 2021, Deep learning for brake squeal: Brake noise detection, characterization and prediction, MECHANICAL SYSTEMS AND SIGNAL PROCESSING, Vol: 149, ISSN: 0888-3270
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- Citations: 19
Nitti A, Stender M, Hoffmann N, et al., 2021, Spatially localized vibrations in a rotor subjected to flutter, NONLINEAR DYNAMICS, Vol: 103, Pages: 309-325, ISSN: 0924-090X
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- Citations: 5
Stender M, Wedler M, Hoffmann N, et al., 2021, Explainable machine learning: A case study on impedance tube measurements
Machine learning techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black-box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepen the understanding on the decision making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficients measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e., how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.
Lünser H, Hartmann M, Desmars N, et al., 2021, Influence of sea state parameters on the accuracy of wave simulations of different complexity
The accurate description of the complex genesis and evolution of ocean waves as well as the associated kinematics and dynamics is indispensable for the design of offshore structures and assessment of marine operations. In the majority of cases, the water wave problem is reduced to potential flow theory on a somehow simplified level. However, the non-linear terms in the surface boundary conditions and the fact that they must be fulfilled on the unknown water surface make the boundary value problem considerably complex. On the one hand, the use of complex methods for solving the boundary value problem may give, at the expense of computational time, a very accurate representation of reality. On the other hand, simplified methods are numerically efficient but may only provide sufficient accuracy for a limited range of applications. This paper investigates the influence of different characteristic sea state parameters on the accuracy of irregular wave field simulations (based on a JONSWAP spectrum) by applying the high-order spectral method. Hereby, the underlying Taylor series expansion is truncated at different orders so that numerical simulations of different complexity can be investigated. The wave steepness, spectral-peak enhancement factor as well as directional spreading are systematically varied and truncation at fourth order serves as reference. It is shown that, for specific parameters in terms of wave steepness, enhancement factor and simulation time, the boundary value problem can be significantly reduced while providing sufficient accuracy.
Desmars N, Hartmann M, Behrendt J, et al., 2021, Reconstruction of ocean surfaces from randomly distributed measurements using a grid-based method
In view of deterministic ocean wave prediction, we introduce and investigate a new method to reconstruct ocean surfaces based on randomly distributed wave measurements. Instead of looking for the optimal parameters of a wave model through the minimization of a cost function, our approach directly solves the free surface dynamics - coupled with an interpolation operator - for the quantities of interest (i.e., surface elevation and velocity potential) at grid points that are used to compute the relevant operators. This method allows a high flexibility in terms of desired accuracy and ensures the physical consistency of the solution. Using the linear wave theory and unidirectional wave fields, we validate the applicability of the proposed method. In particular, we show that our grid-based method is able to reach similar accuracy than the wave-model parameterization method at a reasonable cost.
Ohlsen J, Neidhardt M, Schlaefer A, et al., 2021, Modelling shear wave propagation in soft tissue surrogates using a finite element‐ and finite difference method, PAMM, Vol: 20, ISSN: 1617-7061
Stender M, Hoffmann N, Papangelo A, 2020, The Basin Stability of Bi-Stable Friction-Excited Oscillators, LUBRICANTS, Vol: 8
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- Citations: 4
Tonazzi D, Passafiume M, Papangelo A, et al., 2020, Numerical and experimental analysis of the bi-stable state for frictional continuous system, NONLINEAR DYNAMICS, Vol: 102, Pages: 1361-1374, ISSN: 0924-090X
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- Citations: 4
Papangelo A, Putignano C, Hoffmann N, 2020, Self-excited vibrations due to viscoelastic interactions, MECHANICAL SYSTEMS AND SIGNAL PROCESSING, Vol: 144, ISSN: 0888-3270
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- Citations: 18
Stender M, Jahn M, Hoffmann N, et al., 2020, Hyperchaos co-existing with periodic orbits in a frictional oscillator, JOURNAL OF SOUND AND VIBRATION, Vol: 472, ISSN: 0022-460X
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- Citations: 11
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