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Item type:Publication, Access status: with files , A simulative analysis of physically-motivated Koopman lifted states for wind farm model predictive controlThis paper investigates how the selection of data points from a simulated wind field, combined with nonlinear lifting functions, influences wind turbine power predictions. The objective is to develop accurate linear models within the Koopman operator framework suitable for real-time Model Predictive Control. Using Dynamic Mode Decomposition, we compare three data configurations from a high-fidelity simulation: a dense wind field with 50,000 points, a dataset restricted to turbine locations, and a sparse dataset including two additional streamwise positions. Results demonstrate that models using data from well-placed sensors can outperform those using dense wind field data. Furthermore, while turbine-only data initially yields lower accuracy, we show that physically motivated Koopman lifting functions effectively compensate for missing spatial information. By incorporating these additional prediction features, turbine-only models achieve prediction accuracies comparable to those utilizing the complete wind field data, which relies on information density unavailable for real-world turbine control.Publicationtype: Journal Article9 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Dynamic navigation and port call optimisation in real time(Institute of Physics (IOP), 2026-04); 21 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Towards certifiable autonomous local public transport on waterways(IOP Publishing, 2025-10-01); Autonomous and remotely operated vessels are poised to transform maritime mobility, logistics, and research. This paper presents the MS Wavelab, a comprehensive research platform for intelligent, certifiable, and resilient autonomous surface vessel (ASV) operation in coastal and inland waterways. We introduce a layered navigation framework that integrates sensor fusion, machine learning, and rule-aware path planning to achieve robust situational awareness (SA) and collision avoidance (COLAV) in dynamic maritime environments. To address the lack of domain-specific training data, we curate a high-quality dataset of over 50,000 annotated images from 10,000 hours of video footage collected in real-world operations. This supports a camera-LiDAR fusion pipeline for visual object detection and distance estimation using YOLO-based models. For connectivity, we develop a hybrid communication architecture that combines 5G cellular and Starlink LEO satellite networks. By applying supervised learning models for bandwidth and handover prediction, we achieve stable, low-latency communication essential for teleoperation and high-resolution media streaming. Our system architecture adopts a certification-oriented design, extending type-approved integrated navigation systems with autonomous modules while aligning with emerging international regulatory frameworks such as the IMO MASS Code. Together, these components enable safe, real-time decision-making and remote control in constrained and variable environments like the Kiel Fjord. The MS Wavelab serves as a scalable, modular platform to advance the state of the art in autonomous maritime systems and accelerate their real-world deployment.Publicationtype: Journal Article60 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multi-waypoint global path planning for unmanned surface vehicles in confined environments(IOP Publishing, 2025-10-01); ; ; ; Motivated by the SeaClear2.0 Project's aim to clean ports and coastal areas using aerial and marine robotics, this work focuses on the reference generation for autopilots of Uncrewed Surface Vehicles (USVs) deployed in confined areas, such as, rivers, channels, ports or harbours. In the envisioned scenario, a USV must navigate through multiple waypoints, which roughly sketch the intended route. The planned route is based on available chart data and doesn't take in consideration optimality of the route or uncharted obstacles. The study implements a global path planner consisting in an A*algorithm, extendable via Genetic Algorithm (GA), for particular scenarios, where the order of the given waypoints is not enforced. In addition, a kinematics-based path planner using the Line-of-Sight (LoS) algorithm is developed for creating the reference vector for the USV control system. The global path planner and LoS algorithms are tested both in simulation and in sea trials, using a medium-sized USV. For the simulation environment, a digital twin of the USV is used. The sea trials validate the concept in a representative environment. The entire software solution is developed under Robot Operating System 2 (ROS2) framework and implements Guidance Navigation and Control (GNC) architecture, commonly used for autonomous navigation of USVs. The global path planning algorithm efficiently handles multi-waypoint selection and finds the shortest path between each two waypoints while avoiding known static obstacles. Additionally, the LoS algorithm generates reliable references for the vessel's autopilot. The novelty of the proposed solution consists in the usage of meta-heuristic algorithms for solving a discrete optimisation problem and in the integration of nautical chart data within the global path planner. The paper highlights the potential of the robotic solution for maritime applications, beyond the chosen scenario, when coupled with a complete GNC solution.Publicationtype: Conference Paper33 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , MASS operations in port callsTo achieve success in the maritime industry's shift towards autonomous vessels - seeking the goals of enhanced efficiency, environmental sustainability, and improved safety - it is crucial to develop, test, and validate new procedures and technologies for autonomous ship operations, which includes management of Maritime Autonomous Surface Ships (MASS) at port. So far, the focus of autonomy in maritime has been mainly on the navigational issues, and not as much on the integration between port technology and procedures, and remote operations centre (ROC) operation. MASS will not necessarily have its bridge, cargo handling equipment, and maintenance being manned by onboard agents, but can rather be fully operated and organised by a ROC. To overcome these challenges, there are different areas of concern that must be considered such as navigation and docking, cargo handling, and maintenance, etc.Publicationtype: Conference Paper34 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Next generation maritime workforce planning: Smart scheduling for remote-controlled shipsThe evolving landscape of maritime autonomous surface ships (MASS) is marked by prolonged transitions through various autonomy stages, which necessitate changes in crew composition and qualifications. As crew costs represent a significant portion of operational expenses, quantifying the impact of varying autonomy levels on crew sizes and future qualification requirements is becoming increasingly critical for both system providers and ship operators. This paper introduces a framework designed to evaluate crew demand and scheduling onboard and in fleet operation centers (FOC’s) based on operational concepts such as partially or fully remote-controlled vessels. While few existing studies address this topic, they typically focus on specific aspects of ship operations, such as mooring and watchkeeping, often overlooking the overall workload. In contrast, this paper adopts a holistic approach to represent onboard workload and assess crew demand comprehensively. The framework has been applied to current cargo and cruise liners, providing robust decision-making support in manning strategies. Utilizing mathematical modeling and tailored algorithms, it calculates optimal crew demand and schedules specific to individual ships, voyages, and operations. A task-based methodology incorporates in a holistic manner workload, crew qualifications, and voyage-specific factors, while sorting algorithms generate schedules that comply with complex regulations regarding rest hours and organizational maintenance protocols. The algorithms are tailored to take advantage of the problem structure and scale fast. Companies like Carnival Maritime GmbH have leveraged this tool to optimize crew configurations in response to dynamic operational challenges, such as reduced passenger capacity during the COVID-19 pandemic and the reassignment of ships to new routes. This work demonstrates how the framework can be adapted to offer informed decision support in the MASS sector, particularly for remote-controlled vessels. It outlines how existing information systems can delineate tasks and requisite skills for remote operations, facilitating clear identification of responsibilities transitioning to shore, tasks becoming obsolete, or those undergoing transformation. Furthermore, the paper discusses the integrated holistic planning of ship and shore crews, offering substantial decision-making support for automated vessel providers and FOC operators. Finally, it explores the potential of this model to assess the economic impact of varying levels of automation.Publicationtype: Conference Paper13 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Improved double-point material point method for dynamic geotechnical problemsThe Material Point method is a suitable numerical method to simulate large deformations often occurring in geotechnical engineering. The Double-Point MPM (2P-MPM) extends the simulations for water-saturated materials. In the present work, a coupled dynamic formulation is used to account for coupled wave propagation in porous media in addition to large deformations. The computational cost of 2P-MPM is expensive because information between material points and grid nodes must be approximated at each time step. In conventional 2P-MPM formulations, linear shape functions are used for this approximation, leading to well-known grid crossing and loss of contact problems. We use the moving least square approximation for the 2P-MPM to handle these problems better. In addition, a parallelization approach is shown to improve the computation time, especially for large boundary value problems. The developed methods can be combined with non-saturated materials, such as fluids and dry soil, to simulate complex geotechnical issues. The performed computations of wave propagation and large deformations using the proposed methods show improved convergence behavior and increased efficiency.Publicationtype: Conference Paper12 19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Near-wake measurements and simulations of a floating wind turbine using a four-beam nacelle-based lidarRecent advances in lidar technology have expanded the possibilities for measuring full-scale wind turbine wakes within the atmospheric boundary layer. This study utilizes a four-beam pulsed lidar installed on the nacelle of a floating turbine to measure the near-wake. The wake measurement campaign was conducted on the Floatgen, with lidar measurements reaching up to 2.5 times the rotor diameter distance. These measurements are presented as 10-minute average radial wind speeds. The focus of this study is on the below rated wind speed of 7 m/s. Free flow conditions were assumed at the farthest measurement points, which are located ±0.9 times the rotor diameter from to the rotor center. The sensitivity of lidar measurements to turbulence intensity, vertical wind shear, and significant wave height was investigated. The environmental conditions were categorized into five cases, considering turbulence, vertical shear, and yaw misalignment as well as waves. Two simulation models with different fidelity levels are used to simulate these cases, and the beam-wise radial wind speed predictions are compared with the measurements. A maximum mean absolute error of 11.3% between the measurements and simulations is observed.Publicationtype: Conference Paper26 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The art of orchestrating nautical services in a port call: a literature classification(IOP Publishing, 2024); ; ; Global carbon dioxide emissions are forecast to increase by around 15% by 2050 compared to 2020. In order to move towards a sustainable transition to net zero emissions by 2050, more energy-efficient operations are a prerequisite. In response, terminals are looking for ways to optimise port processes to reduce energy consumption while also keeping waiting time and costs low. This paper focuses on optimisation approaches for the individual actors in the nautical service that can support these objectives. A comprehensive review, classification and discussion of the relevant literature on current methods and procedures for the stakeholders of the nautical service is conducted. In this regard, current challenges and limitations are identified. The results of the classification scheme show that tugboats in particular are examined in more detail. The objective is often to reduce costs and increase safety in the port area. For this purpose, mathematical approaches, or various types of algorithms are usually applied. Future research activities could be based on more detailed analyses of the approaches used in the identified papers.Publicationtype: Conference Paper94 34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Prediction of induced soil vibration during pile vibrodriving using Dynamic Mode Decomposition (DMD)(IOP Publishing, 2024); ; ; This study investigates using the Dynamic Mode Decomposition (DMD) algorithm to perform approximations and time-ahead prediction of soil vibrations during the vibrodriving process. Geotechnical applications face challenges in modeling and predicting soil vibrations due to the soil's heterogeneous nature. This study addresses this issue using a purely data-driven approach. Geophone data collected during pile installation using a vibrodriver were used to demonstrate the feasibility of the DMD algorithm. The research reveals that both the standard DMD and augmented DMD, which incorporate delay coordinates, can achieve accurate predictions, with the augmented DMD producing more accurate time-ahead predictions of the vibrations. The results emphasize the potential practical utility of data-driven methods for vibration prediction in geotechnical applications.Publicationtype: Conference Paper9 9