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Item type:Publication, Access status: without files , Torsional galloping with magnetic repulsion vertical axis current turbine(Elsevier, 2026-10-21); ; The article proposes an innovative concept of a current turbine based on the torsional galloping phenomenon, combined with the magnetic repulsion of a streamlined thin vertical plate, intended to harvest energy from low-head water currents. The driving proposal is to exploit the inherent instability of the arrangement. Hence, a careful non-linear instability analysis using the Poincaré method was performed numerically and experimentally. The mathematical model based on Bessel functions and potential theory was validated experimentally. The study carried out different configurations, comprising different stability conditions. The variances are selected by variance analysis. Since energy extraction is performed due to its unstable behavior, the occurrence of a stable condition must be avoided. It has been possible to select the condition where the turbine presents the most significant instability and the one that will provide higher efficiency. A final experimental set-up reached conclusive and successful feasibility.Publicationtype: Journal Article156 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Controlled weakening of model ice: Replicating the mechanical response of summer-warmed sea ice in laboratories and test basinsThis study examines how the mechanical properties of model ice can be tuned to approximate the weakening of natural sea ice as it warms during summer. The work is motivated by satellite-derived observations and projections indicating that warm-ice conditions in the Arctic are increasing. The observed warming trend, coupled with the seasonal timing of Arctic shipping activities, challenges the traditional focus on winter conditions in model tests and suggests that incorporating weaker ice is increasingly relevant to ship–ice interaction and ship design. Laboratory tests for ship-performance optimization are often a crucial intermediate step toward implementation in the final maritime application. To provide a broader range of test conditions, we explore methods to adjust two key parameters – the elastic modulus and flexural strength – in model ice under laboratory conditions. The overarching goal is to establish a testing regime capable of representing selected summertime sea-ice properties. Experiments conducted at the Hamburg Ship Model Basin demonstrate that surface treatment of columnar model ice with saturated sodium chloride solution effectively reduces its flexural strength by 71%. Furthermore, we investigate how variations in the amount of applied solution influence mechanical properties, highlighting differing sensitivities of elastic modulus and flexural strength to modifications of the ice surface layer.Publicationtype: Journal Article17 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Regularized machine learning for system identification of ship free-running manoeuvres from CFD-based synthetic data: a comparative studyThis study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations. Specifically, ordinary least squares and regularized regression methods are applied to Abkowitz-type manoeuvring models. Training and validation datasets are derived from URANS simulations of zig-zag and turning circle manoeuvres, which are validated against experimental benchmark data. The analysis evaluates the effects of coefficient set size, minimum training length required for predictive model training, and manoeuvre combinations on model performance. Results demonstrate the suitability of large-angle zig-zag manoeuvres for hydrodynamic system identification, provided that multicollinearity is addressed through appropriate coefficient selection, regression models, or input data variability. Larger coefficient sets offer greater model flexibility for variable conditions but are more prone to multicollinearity. Regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy, as does incorporating more diverse manoeuvring data. Among tested models, Ridge regression provided the best compromise between computational efficiency and prediction accuracy.Publicationtype: Journal Article31 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Experimental and numerical investigation of free-running turning manoeuvres in extreme shallow waterThe precise prediction of ship manoeuvring behaviour in shallow water is essential for safe navigation and waterway design. To examine depth-induced changes in ship dynamics, the turning behaviour of the container ship DTC (Duisburg Test Case) was investigated at model scale (1:60) using combined free-running experiments and CFD simulations, with emphasis on extreme shallow water down to (Formula presented) .Free-running manoeuvres were conducted, with measurement of propeller thrust and rudder forces. The CFD model was verified by means of a grid convergence study for a complete turning manoeuvre and validated against experimental data, showing mean deviations of approximately 5% for key manoeuvring parameters, confirming its predictive capability.The results reveal a pronounced deterioration of steady turning performance with decreasing water depth. A reversal in port-starboard asymmetry occurs in shallow water, governed by depth-dependent changes in propeller-rudder interaction and wake distribution. Transient analysis further shows that initial turning ability increases slightly in the transition from (Formula presented) to 1.15, but deteriorates sharply at (Formula presented), identifying a critical threshold. These effects are linked to redistributions of hydrodynamic forces along the hull.The results highlight the coupled influence of water depth and speed, emphasizing the governing role of the depth Froude number on manoeuvring behaviour.Publicationtype: Journal Article25 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A novel hybrid physics-data driven encoder-decoder model for wind turbine wake predictionsFast and accurate monitoring or prediction of wind turbine wakes is essential for real-time optimization of wind farm power generation. However, conventional approaches face significant challenges: direct field measurements using instruments like LiDAR are often economically infeasible due to high costs, high-fidelity numerical simulations are computationally prohibitive and slow for real-time applications, and purely data-driven machine learning methods typically suffer from poor physical interpretability and uncontrolled errors in full-field predictions. To address these limitations, we propose wakeEvoNet, a novel hybrid physics-data driven framework for real-time wind turbine wake monitoring/prediction. This approach strategically deploys sparse but high-fidelity measurement points in the near-wake region (within 3 rotor diameters downstream) to capture flow velocity profiles, which are then used as input for convolutional neural network (CNN) based encoder-decoder models to infer the downstream wake field (from 4D to 14D downstream). By feeding real-time monitored data at each time step, the model prevents temporal error accumulation common in sequential predictions. This hybrid methodology—combining direct measurements in the near-wake with neural network-based evolution for the far-wake—significantly reduces the number of required instruments while maintaining high accuracy across the entire wake field. In this study, we demonstrate the framework using the NREL 5 MW reference turbine under neutral atmospheric boundary layer conditions. High-fidelity computational fluid dynamics simulations employing large eddy simulation and the actuator line method are leveraged to generate the training datasets, and three convolutional neural network-based encoder-decoder architectures are developed: a benchmark U-Net (wakeEvoNet-1), a CBAM-enhanced U-Net (wakeEvoNet-2), and a novel CA-TA-UNet (wakeEvoNet-3) incorporating a physics-guided lightweight spatial attention mechanism tailored for wind turbine wake flows. The models utilize three consecutive upstream cross-sections to predict subsequent downstream fields, with results showing high accuracy (R2 scores consistently above 0.80) and robust temporal performance on test data, offering an efficient alternative to traditional methods for real-time wind turbine wake monitoring/prediction.Publicationtype: Journal Article15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Hydrodynamic performance of oscillating water column wave energy converter arrays of offshore hybrid aquaculture–energy platformsOffshore aquaculture faces a critical challenge in securing a sustainable and reliable power supply due to its increasing energy demands. Ensuring both structural compatibility and efficient energy generation remains a significant barrier. The rigid geometry of floating aquaculture cage structure permits the installation of multiple Oscillating Water Column Wave Energy Converters (OWC–WECs) along their perimeter, thereby enhancing energy capture through array configurations. This study presents a numerical investigation of OWC–WEC arrays integrated with a floating aquaculture cage. A three-dimensional RANS–VOF framework, incorporating an advanced forcing-zone method and finite-volume discretization, was developed to evaluate the hydrodynamic performance of the hybrid system under regular and irregular waves. Convergence analyses were carried out, and the numerical model was validated against available experimental data, demonstrating good agreement. Using this validated framework, a systematic evaluation of different array layouts was performed to examine the influence of device quantity and spatial arrangement on system performance. Both single- and dual-chamber configurations, positioned adjacent to the aquaculture cage, were simulated to assess pneumatic power output and platform motion response. The influence of mooring pretension was also investigated, highlighting its combined effect with array configuration on optimizing energy performance. Overall, the findings establish a structured methodology for assessing and optimizing hybrid marine systems that integrate sustainable aquaculture with wave energy harvesting.Publicationtype: Journal Article35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , On the singularity and numerical instability of flow fields associated with the nonlinear Schrödinger equationThe nonlinear Schrödinger equation (NLS) is a well-known equation in the study of wave motion. In the context of nonlinear water waves, the NLS has been proven to accurately model deep-water waves with a narrow spectral bandwidth and moderate wave steepness. While many studies have focused on reconstructing free surface profiles using the NLS, the associated flow field within the fluid has received less attention. Recently, a paper was published in which the flow field associated with solutions of the NLS was constructed. Here, it has been noted that the associated modeled flow field can contain singularities below the water surface. This work analyzes the occurrence and location of such artificial singularities for different analytical NLS solutions. It is found that the flow field can generally become singular at any depth below the surface and may even appear periodically in water depth. Additionally, it is shown that the numerical computation of the flow field poses considerable challenges due to the instability of numerical schemes. These findings are crucial for the accurate analysis of flow fields corresponding to the NLS.Publicationtype: Journal Article53 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Accelerating the solution of Poisson equation in MPS method for tank sloshing using graph attention networkThe Moving Particle Semi-implicit (MPS) method is a widely used Lagrangian Particle method for simulating free-surface flows. In conventional MPS simulations, the solution of the pressure Poisson equation (PPE) in the pressure-projection step typically constitutes a major portion of the overall computational cost, particularly for large-scale particle systems. To address this issue, we propose a data-driven hybrid approach that integrates the MPS method with the Graph Attention Network (GAT), denoted as MPS-GAT. In this approach, GAT serves as a surrogate model to replace the traditional PPE solver for pressure estimation in MPS. The effectiveness of the proposed method is evaluated using a benchmark case of tank sloshing. The accuracy is verified by comparing the pressure predictions from MPS-GAT with numerical results obtained from solving the PPE. Spatial errors are quantified by differences in mesh-interpolated mean fields between MPS-GAT and traditional MPS method, and temporal errors by the discrepancy in the mean particle velocity and pressure over time. To evaluate generalization, the model is tested on sloshing scenarios with filling ratios, excitation frequencies, and amplitudes not included in the training data. Remarkably, the model exhibited exceptional generalization performance under internal-obstacle configurations despite their complete absence from the training distribution. Furthermore, the proposed hybrid method significantly improves computational efficiency. In our tests with particle counts from 1000 to 130000, MPS-GAT reduces the pressure estimation time by more than 30 times compared with the traditional MPS method when the particle number exceeds 120000. The speedup ratio increases further with larger particle counts, demonstrating clear advantages for large scale free surface flow simulations.Publicationtype: Journal Article18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A multi-resolution MPS-PD coupling method for three-dimensional fluid-structure interaction involving fracture(2026-01-15); ; ; In this paper, an in-house solver MPSPD-SJTU is developed to address 3D Fluid-Structure Interaction (FSI) problems with fracture. The Moving Particle Semi-implicit (MPS) method is utilized to simulate the violent free-surface flows while the PeriDynamics (PD) method is employed to model the structures with large deformations and fractures. Besides, a Kernel Function Based Interpolation Technique (KFBI) is incorporated into the coupling method to handle the MPS particles and PD particles with different resolutions. Firstly, the MPS solver is utilized to simulate dam break flow impacting a fixed obstacle, serving to evaluate its accuracy and reliability. Subsequently, the PD-based structural solver is validated by simulating an oscillating cantilever plate, a clamped-clamped elastic beam under uniformly distributed load, the Kalthoff-Winkler impact test, and three-point bending of an ice beam. Next, the MPS-PD model is validated via some benchmark tests, such as flood discharge with an elastic plate, hydrostatic water column on an elastic plate, dam-break flow with a hanging elastic plate, as well as dam-break flow with an elastic obstacle. The numerical results obtained in this study exhibit good agreement with experimental data as well as those from other numerical methods. Finally, the coupling model is applied to investigate the dam-break flow impacting a hanging elastic plate with a prefabricated crack.Publicationtype: Journal Article31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A computational method for the fluid-structure interaction in nonlinear ocean waves using the nonlinear Schrödinger equationAn efficient computation of the fluid-structure interaction (FSI) between mechanical structures and ocean waves is an important topic for many applications in ocean engineering and naval architecture. For many of such applications, it is often sufficient to consider the FSI within the potential flow theory instead of using the Navier-Stokes equations. However, the numerical solution of the governing equations of the potential flow theory is still computationally expensive. This work contributes to the improvement of the currently available numerical schemes, which compute the FSI in nonlinear ocean waves. The approach used in this work is based on the nonlinear Schrödinger equation (NLS). Compared to the governing equations of potential flow theory, solutions of the NLS can be computed much more efficiently. This efficiency advantage is used to develop a method that allows a fast computation of the FSI in nonlinear water waves. The developed method is investigated with regard to its accuracy, application areas, and computational effort. Here, numerical results for the hydrodynamic forces acting on mechanical structures and the resulting motion of the structures are analyzed. It is found that the NLS offers an interesting possibility to efficiently investigate the behavior of structures excited by nonlinear ocean waves.Publicationtype: Journal Article81 31