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Item type:Publication, Access status: with files , Workshop numerical methods in geotechnics: AI in geotechnics, deformation based design, numerical optimization(Technische Universität Hamburg, 2026-09-17); ; ; ; The Workshop “Numerical Methods in Geotechnics”, held on 17–18 September 2026 in Hamburg, Germany, brings together researchers and practitioners to discuss recent developments in computational geotechnics. The workshop focuses on three main topics: artificial intelligence in geotechnics, deformation-based design, and numerical optimization. The contributions presented in these proceedings cover data-driven and AI-assisted modelling, surrogate models, advanced finite element methods, uncertainty quantification, soil–structure interaction, and optimization approaches in geotechnical engineering. Together, they highlight current advances in numerical methods and their growing importance for more predictive, efficient, and reliable geotechnical analysis and design.Publicationtype: Conference Proceedings125 66 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , From internal resonance to kinking and stacking in the response of nonlinear oscillatorsIn nonlinear dynamical systems, internal resonance is a well known phenomenon. Natural frequencies may change with vibration amplitude, becoming commensurable or close at certain energy levels. Nonlinear modes then interact based on their participating oscillators. Here, we investigate effects on the response curve of a system as neighboring oscillators are modified. This provides new insights into the relation of internal resonance and the frequency response of coupled nonlinear systems. We use methods of Harmonic Balance and a continuation algorithm to compute nonlinear frequency response curves and nonlinear normal modes for a model system. It is arranged as a chain of Duffing oscillators with separated natural frequencies. We demonstrate a kinking effect in the response curve once internal resonance emerges in the system’s forced response. The kink is observed as a shift to higher response levels, stretching the response curve. We also show how this kinking effect is modified by adapting the nonlinearities of neighboring oscillators. The system’s response branches kink vertically as an oscillator in the chain is made linear, stacking on top of each other. Multiple stable solutions at a certain frequency may then be observed. We show that these solutions correspond to varying degrees of vibration localization in the system. We expect our results to be valuable in system design. It should be possible to exploit the observed kinking effect, shaping the response curve by adding suitable oscillators of various characteristics. The engineer may thus be able to reach a target system response level at a certain frequency.Publicationtype: Journal Article39 46 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Experimental evaluation of data-driven deterministic wave generation(American Society of Mechanical Engineers (ASME), 2026-06); ; ; ; This paper explores the applicability of data-driven deterministic wave generation in an experimental environment. Various datasets that comprise different combinations of experimental- and synthetic data are used to train a fully convolutional neural network on the task of predicting the wave-maker control signal associated to a desired target wave sequence at a predefined target location in the wave tank. The trained models are evaluated in an experimental program, where the predicted control signals are executed in a wave tank, the actual wave is measured and compared against the predefined target wave. The standard wave modeling approach for the generation of tailored wave sequences, which is based on linear theory, is conducted for reference. The test dataset contains 950 irregular sea states with a wide range of characteristic sea state parameters, as well as seven tailored extreme waves, including the well-known “Draupner Wave”. It is shown that the data-driven approach consistently outperforms the standard method in accuracy across the studied range of sea states, emphasizing its effectiveness as a flexible and precise alternative for wave basin experiments.Publicationtype: Conference Paper29 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Angle modulation phase shift in vibro-acoustic modulation: a novel approach for early crack detection(Multidisciplinary Digital Publishing Institute (MDPI), 2026-01-09); ; Detecting structural defects is one of the primary challenges engineers face. Consequently, the development of techniques and methods capable of detecting structural defects has always been critical. It should be emphasized that crack detection is only meaningful if it occurs before the final stages of structural failure. Accordingly, the early identification of structural defects has become a significant research challenge, motivating the development of techniques and diagnostic parameters that can effectively capture and reflect the structure’s nonlinearity or non-uniform behavior. This study aims to provide a more detailed examination of modulation phenomena observed in the measured response using the vibro-acoustic modulation (VAM) method, and propose a new model that simultaneously incorporates all three conventional modulation types (amplitude, frequency, and phase), which may offer a more accurate representation of the response signal behavior. Both theoretical and experimental results clearly confirm that the phase shifts of individual frequency components in the frequency domain vary throughout the lifetime of the tested specimen. This behavior, as anticipated by the proposed model, reveals a strong correlation between phase shifts and modulation indices (MIs). Furthermore, the relative sensitivity analysis indicates that the phase shift is more sensitive than the modulation index (MI), suggesting its strong potential as an indicator for early defect detection in structural components.Publicationtype: Journal Article17 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A network‐based approach to identifying key components in structural vibrationsModeling the dynamics of systems with many interacting components, such as robots, wind turbines, and trusses, remains challenging today. These systems often display complex oscillatory responses to external inputs, and harmful vibrations might be excited along with the desired motion. Understanding the relative importance of individual components or systems aspects to the overall system dynamics could be a vital step towards focused design and maintenance efforts. This work proposes a network‐based approach to studying the dynamics of a mechanical system by representing the system as a network of coupled oscillators, where each node corresponds to a machine component and each link denotes a physical connection, such as a weld or bolt. Inspired by studies of dynamics in biological and social networks, we show how network measures can be used to predict the importance of a single oscillator, or component, for shaping the overall dynamics. We further demonstrate under which conditions these conclusions are possible, and where the metrics fail. This study hopes to contribute to the broader field of network‐based methods in engineering and yield insights that help focus design and maintenance efforts in the future.Publicationtype: Journal Article21 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Exploitation of modal superposition toward forced vibration localization in a coupled symmetric oscillator array(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-14); ; ; In transducer arrays, symmetric grouping of identical elements is often employed to achieve uniform array performance. Such arrays can possess high coupling, preventing localized operation of individual transducers. This paper provides insight into how forced vibration localizes in a symmetric system of coupled oscillators. We use a simple lumped-parameter model of highly coupled oscillators derived from ultrasound transducer arrays. Forced vibration localization can be shown to be inversely related to the coupling strength between the oscillators. The results demonstrate how forced vibration in a coupled symmetric system may localize through modal superposition and how it may be tuned via the spacing of natural frequencies. Breaking the system’s symmetry leads to normal mode localization, which can be shown to affect the forced vibration response. The results reveal a variation in the system’s resonance frequency, attributed to curve veering effects.Publicationtype: Journal Article25 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The influence of the modeling order on the predictable area of non-linear unidirectional ocean waves(Elsevier, 2025-04-30); ; ; ; The influence of non-linear modeling of phase-resolved ocean wave fields on the extent of the accessible predictable area is investigated. Assuming that the ocean surface dynamics is known over a limited spatial domain, e.g. via radar backscatter reconstruction, the linear wave theory as well as the high-order spectral method with various orders of non-linearity are used to propagate the surface with different levels of physical fidelity. The prediction accuracy is quantified by comparing the predicted waves to a reference, i.e. a fully known wave field propagated with a high-fidelity wave model. By doing this, it is made possible to track the spatiotemporal evolution of the prediction accuracy and define the predictable area as the region over which the accuracy is higher than a threshold, here defined by a “surface similarity parameter” lower than 0.1. Different unidirectional wave field characteristics are studied, highlighting the effect of the wave steepness, water depth and wave energy spreading around the peak spectral frequency, all impacting significantly the prediction accuracy, thus the predictable area. It is shown that the extent of the predictable area is highly dependent on the order of the considered wave model, and that the third order generally leads to the largest reachable predictable area in all configurations.Publicationtype: Journal Article15 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships(Elsevier, 2025-02-01); ; ; ; The accurate prediction of vessel responses in waves is crucial for decision-making and contribute to the operational safety and risk minimization. Short-term predictions can be carried out by estimating the vessel's motions and loads based on incident waves. Existing model-based approaches either require computationally intensive simulations that compromise real-time capability or use simplified models affecting the accuracy of the prediction. Therefore, this study explores the feasibility of using neural networks for mapping time signals of surface elevation data and a set of corresponding ship responses, i.e. the heave and pitch motions as well as the vertical bending moment. The approach followed here is built on the assumption that the wave profile amidships is known. A synthetic dataset was generated using a time-domain strip theory solver with considerations of non-linear effects on motions and loads due to large amplitude waves in a variety of irregular, long-crested sea state conditions. We propose two different neural network models, a multi-layer perceptron (MLP) and a fully convolutional neural network (FCNN), and compare their performances on measurement data obtained from model tests in a seakeeping basin. The evaluations also include the freak wave reproduction of the ‘new year wave’. The proposed networks are able to estimate the motions and bending moment accurately for a wide range of sea state conditions, surpassing current state-of-the-art models on the given data sets.Publicationtype: Journal Article78 97 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , In Situ Operando Indicator of Dry Friction Squeal(Multidisciplinary Digital Publishing Institute (MDPI), 2024-12-08); ; ; In various applications, dry friction could induce vibrations. A well-known example is frictional braking systems in ground transportation vehicles involving a sliding contact between a rotating and a stationary part. In such scenarios, the emission of high-intensity noise, commonly known as squeal, can present human health risks based on the noise’s intensity, frequency, and occurrences. Despite the importance of squeal in the context of advancing urbanization, the parameters determining its occurrence remain uncertain due to the complexity of the involved phenomena. This study aims to identify a relevant operando indicator for predicting squeal occurrences. To this end, a pin-on-disc test rig was developed to replicate various contact conditions found in road profiles and investigate resulting squealing. Each test involves a multimodal instrumentation, complemented by surface observations. It is illustrated that the enhanced thermal indicator identified is relevant because it is sensitive to the thermomechanical and tribological phenomena involved in squealing.Publicationtype: Journal Article9 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks(Multidisciplinary Digital Publishing Institute (MDPI), 2024-10-01); ; ; The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN’s loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods.Publicationtype: Journal Article31 38