192 results found
Stender M, Hoffmann N, 2024, The Role of Damping in Complex Structural Dynamics: Data-Driven Approaches, Lecture Notes in Applied and Computational Mechanics, Pages: 83-104
The Dynamics Group at Hamburg University of Technology has been working on two consecutive research projects supervised by Prof. Norbert Hoffmann within the Priority Programme SPP 1897. The first project ‘Understanding and improving energy dissipation and damping in structures subject to self-excited irregular vibrations’ focused on the chaotic nature of friction-excited dynamics and how properties of those complex vibrations can be leveraged for understanding damping and stability. Within the second project ’Understanding and improving energy dissipation and vibration damping in structures subject to self-excited irregular vibrations - linking data driven approaches with modelling’, data-driven techniques were linked with conventional modeling approaches to arrive at hybrid simulation and identification approaches for damping in complex structural dynamics. The work at hand summarizes central findings and novel approaches that have been published in a number of peer-reviewed journal articles, poses new research questions, and gives an outlook.
Ehlers S, Klein M, Heinlein A, et al., 2023, Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data, Ocean Engineering, Vol: 288, ISSN: 0029-8018
Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive optimization procedures or simplistic modelling assumptions that compromise the real-time capability or accuracy of the subsequent prediction process. We therefore address these issues by proposing a novel approach for phase-resolved wave surface reconstruction using neural networks based on the U-Net and Fourier neural operator (FNO) architectures. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids, that is generated by the high-order spectral method for wave simulation and a geometric radar modelling approach. The investigation reveals that both models deliver accurate wave reconstruction results and show good generalization for different sea states when trained with spatio-temporal radar data containing multiple historic radar snapshots in each input. Notably, the FNO demonstrates superior performance in handling the data structure imposed by wave physics due to its global approach to learn the mapping between input and output in Fourier space.
Desmars N, Hartmann M, Behrendt J, et al., 2023, Nonlinear deterministic reconstruction and prediction of remotely measured ocean surface waves, Journal of Fluid Mechanics, Vol: 975, ISSN: 0022-1120
Algorithms for reconstructing and predicting nonlinear ocean wave fields from remote measurements are presented. Three types of synthetic observations are used to quantify the influence of remote measurement modulation mechanisms on the algorithms' performance. First, the observations correspond to randomly distributed surface elevations. Then, they are related to a marine radar model - the second type takes the wave shadowing modulation into account whereas the third one also includes the tilt modulation. The observations are numerically generated based on unidirectional waves of various steepness values. Linear and weakly nonlinear prediction algorithms based on analytical models are considered, as well as a highly nonlinear algorithm relying on the high-order spectral (HOS) method. Reconstructing surfaces from shadowed observations is found to have an impact limited to the non-visible regions, while tilt modulation affects the reconstruction more generally due to the indirect, more complex extraction of wave information. It is shown that the accuracy of the surface reconstruction mainly depends on the correct modelling of the wave shape nonlinearities. Modelling the nonlinear correction of the dispersion relation, in particular the frequency-dependent wave phase effects in the case of irregular waves, substantially improves the prediction. The suitability of the algorithms for severe wave conditions in finite depth and using non-perfect observations is assessed through wave tank experiments. It shows that only the third-order HOS solution predicts the right amplitude and phase of an emerging extreme wave, emphasizing the relevance of the corresponding physical modelling.
Wedler M, Stender M, Klein M, et al., 2023, Machine learning simulation of one-dimensional deterministic water wave propagation, OCEAN ENGINEERING, Vol: 284, ISSN: 0029-8018
Geier C, Hamdi S, Chancelier T, et al., 2023, Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations, NONLINEAR DYNAMICS, ISSN: 0924-090X
Geier C, Stender M, Hoffmann N, 2023, Data-driven reduced order modeling for mechanical oscillators using Koopman approaches, FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, Vol: 9
Stender M, Ohlsen J, Geisler H, et al., 2023, U<SUP>P</SUP>-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics, COMPUTATIONAL MECHANICS, ISSN: 0178-7675
Vater K, Stender M, Hoffmann N, 2023, Towards neural network‐based numerical friction models, PAMM, Vol: 22, ISSN: 1617-7061
<jats:title>Abstract</jats:title><jats:p>Friction contacts can be found in almost all mechanical systems and are often of great technical importance. However, they are usually difficult to describe, and their behavior and influence on the whole system are hard to predict accurately. Modern product design and system operation strongly benefit from numerical simulation approaches today, but reliable friction models still represent a major challenge in this context.</jats:p><jats:p>To tackle this problem, we employ neural network regression to capture the characteristics of frictional contacts and make them accessible for numerical methods in a minimal intrusive fashion. In particular, we test our approach using a Finite Element model of a 2D cantilever beam subject to stick‐slip vibrations induced by a moving conveyor belt at its free end. As a reference solution, we perform a transient analysis based on a simple analytical friction model, where the kinetic friction force only depends on the normal load and the relative sliding velocity. We take the same friction model, add some artificial noise to mimic uncertainties coming with experimental measurements, and pick a limited set of data points to train a regression neural network. The machine learning friction model is then deployed in the Finite Element code to predict the kinetic friction force acting on the beam tip during the slip phases.</jats:p><jats:p>The deflection curves obtained by the transient numerical analysis using the new neural network friction model agree well with the reference solution based on the underlying analytical model. The results indicate that data‐driven approaches may also be capable of capturing more complex frictional contacts, including effects of temperature, humidity, and load history. The trained neural network friction models can then be employed in numerical simulations in a minimally intrusive manner. This approach opens up new possibili
Deutzer M, Stender M, Tuepker N, et al., 2023, 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, Pages: 53-72, ISSN: 2770-3460
Wedler M, Stender M, Klein M, et al., 2022, Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data, NEURAL NETWORKS, Vol: 156, Pages: 123-134, ISSN: 0893-6080
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 TRANSACTIONS, Vol: 129, Pages: 675-686, ISSN: 0019-0578
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.
Stender M, Hoffmann N, 2022, 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
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
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
Klein M, Clauss GF, Hoffmann N, 2021, Introducing envelope soliton solutions for wave-structure investigations, OCEAN ENGINEERING, Vol: 234, ISSN: 0029-8018
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>
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.
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
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
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
Rudorf M, Oberst S, Stender M, et al., 2021, Bifurcation Analysis of a Doubly Curved Thin Shell Considering Inertial Effects, Vibration Engineering for a Sustainable Future: Numerical and Analytical Methods to Study Dynamical Systems, Vol. 3, Pages: 51-57, ISBN: 9783030464653
Thin elastic structures are found in nature as well as in technical applications. Examples are plant materials (leaves) or insect appendages (wings), parts of instrumentation (optical mirrors, membranes) or vehicles components (solar panels/antennas in satellites or car and aircraft bodies). Numerical modelling of those structures is commonly conducted using shell elements. Especially doubly curved shells have found much attention due to their applicability in thin shell or sandwich structures used in the automotive, aerospace and space industry. In the design process, it is generally assumed that these structures behave linearly; however, considering their curvature and how thin they are, large deflections easily become an issue as shown experimentally. Yet, the numerical modelling does conventionally assume that inertia effects can be neglected. Here we derive the equations of motion of a simply supported configuration of a doubly curved shell with 9 degrees of freedom with and without inertial coupling terms. We show by conducting a bifurcation analysis that the additional inertia effects cannot be neglected and that care has to be taken when structures as such are being employed as appendages on real-life satellites.
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