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    Autonom und kooperativ agierende Module zur kommandierten Schiffsassistenz in Häfen
    Die Entwicklung der autonomen Schifffahrt schreitet weltweit schnell voran. Erste autonome Schiffe fahren bereits im Regelbetrieb. Dahingegen ist die hafenseitige Manöverassistenz der Teil der Transportkette, der derzeit noch komplett manuell ausgeführt wird. Ziel des Vorhabens ist die Entwicklung eines autonomen, modularen Assistenzsystems, bestehend aus einer variablen Anzahl von elektrisch angetriebenen Schleppermodulen und einer mobilen Steuerbox. Dieses soll auf Grundlage der kommandierten Anforderungen des Hafenlotsen bzw. des Kapitäns des zu assistierenden Schiffes, auf einfache und intuitive Weise Unterstützung beim Manövrieren im Hafen bieten. Dazu werden die benötigten Assistenzmodule konzipiert, deren autonome Steuer- und Regelsysteme mithilfe von zu entwickelten numerischen Simulationsverfahren ausgelegt und im Modellmaßstab erprobt. Die Fähigkeiten des entwickelten Assistenzsystems wird anschließend unter Realbedingungen im Hafen demonstriert. Das geplante Assistenzsystem bietet der Hafenwirtschaft eine effiziente und umweltgerechte Technologie zur Abfertigung konventioneller und autonomer Schiffe. Dies wird zukünftig ein deutlicher Wettbewerbsvorteil in der Konkurrenz internationaler Häfen sein.
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    RetroLadung – Development of simulation and AI-based Methods for increased Loading Safety of Container Transportation
    Background and Objectives Every year, several hundred containers are lost at sea, causing environmental pollution and endangers for shipping traffic. Most containers go overbord because of insufficient lashing or ship operation in severe environmental conditions leading to exceedance of the dimensioning loads of the lashing equipment. Therefore, technical solutions for safe and efficient container transport are needed. In the joint research project RetroLadung, a modular cell guide system with an integrated sensor network is developed. As the weight of the installed cell guide system reduced the vessel’s payload, the additional cell guides must be as light as possible. For the dimensioning and weight optimization of the cell guide structures, the occurring loads must be known exactly. During ship operations, an artificial intelligence-based monitoring and decision support system coupled to the integrated sensor network guarantee that the loads stay within acceptable limits. Approach Within the research project RetroLadung-EsiKIEL, simulation and artificial intelligence-based methods for the improvement of the operational safety of container transportation are developed. In the first stage, an efficient non-linear time domain motion simulation method is used to determine the wave induced motions and accelerations at container stowage positions needed for structural dimension and optimization of the cell guide system. Extensive model tests will be carried out at the towing tank to validate the simulation results. Main objective of RetroLadung-EsiKIEL is the development of an artificial intelligence-based monitoring and assistance system to ensure that the operational limits of the cell guide system are not exceeded during ship operation. Based on acceleration measurements at the cell guides, the operational status is evaluated and recommendations for safe ship operation are given if necessary, e.g. regarding forward speed and course of the vessel. Application and Examples In a first step, the ship models and environmental and operating conditions to be investigated are specified. The project focusses on two relevant industrial application cases: Container transportation at the American east coast using towed barges and midsized open top container vessels. For the simulation-based assessment of wave induced loads, a sequential process is developed. Since the dimensioning of the cell guide structures requires reliable statements about the occurring loads at a very early stage of the project, an efficient linear frequency domain method is applied first. In the next step, a non-linear time domain simulation method is used for a more detailed motions analysis. Additionally, finite volume methods are used to determine direct loads, e.g. from wave impacts in extreme situations. As only very little validation data is available for the investigated cases, model tests are carried out in the institute’s wave basin to evaluate the accuracy of the applied numerical methods. In the second phase of RetroLadung-EsiKIEL the monitoring and assistance system is developed. For the modelling of the complex dynamic behavior of the vessels, artificial neural networks (ANN) are used. Based on the measurement data from the integrated sensor network of the cell guides, e.g. accelerations at the container stowage positions and stack weights, the operational condition is monitored and evaluated. If potential exceedance of the operational limits is detected, recommendations for safe ship operation are provided. Therefore, separate ANN-based methods for short time forecasting and decision support are implemented. For the initial training and design of the ANNs the motion simulation results are used. Because of the high variability of the dynamic behavior of the vessel, machine learning methods for run-time adjustment using measurement data and online co-simulation must be developed and tested.
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