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    Nutzung multipler Datenströme zur Ableitung von adaptiven Handlungsempfehlungen im Seegang
    Container losses at sea are estimated at several thousand per year. Fires that are not detected in time and cargo going overboard as a result of heavy weather events not only lead to loss of value, but are also a danger to the crew, the environment and other watercraft. Further mechanical reinforcement of cargo securing is both technically and economically impractical and is not a solution to undetected fires. The innovative goal of the CMS - Container Monitoring System project is to substantially reduce container losses by digitizing cargo securing. An assistance system warns of fire sources at an early stage and provides the crew with recommendations for action to prevent the container stacks from building up. This is based on sensors distributed and networked throughout the cargo as well as model-based evaluation and prediction processes. Monitoring of the cargo on the ship's deck is made possible by instrumenting the securing elements (twistlocks) between the containers with robust and durable sensor technology. These smart twistlocks will be equipped with highly efficient wireless technology, allowing for several years of battery life and reliable data transmission in the highly shielded environment. Predictive evaluation methods that predict critical conditions in near-real time are a prerequisite for timely intervention by the crew. For this purpose, the acquired sensor data are combined with a hydrodynamic ship model, a hybrid weather model and a highly specialized stack model. For early fire detection, small temperature changes inside the container are separated from other temperature influences, such as solar radiation, based on the model. It is expected that cargo losses can be massively reduced in the future by these measures without influencing loading and unloading processes. Furthermore, the integrated load measurement enables the recording of real container masses during loading as well as the detection of container losses during the sea voyage.
      53  15
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    Equitable RESilience solutions to strengthen the link between CUltural landscapEs and coMmunitiEs
    Innovative solutions to protect Europe’s cultural heritage. Cultural heritage is an important aspect of our communities and economies, but it is vulnerable to climate change and other risks like pollution and over-tourism. Therefore, fostering resilience is essential. In this context, the EU-funded RescueME project will show the links between cultural landscapes and communities. It will demonstrate that an innovative data-driven, community-based, heritage-centric actionable landscape approach to resilience enhancement can protect our cultural heritage and support the transition to a green society and economy. Specifically, the project will test the effectiveness of an actionable framework based on the resilient historical landscape approach complemented by data, models, methods and tools to protect European cultural heritage. Five case studies will be carried out at European coastal landscapes.
      47
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    Fuselage, Rear Fuselage and Empennage with Cabin and Cargo Architecture Solution validation and Technologies for H2 integration
    The FASTER-H2 project will validate, down select, mature and demonstrate key technologies and provide the architectural integration of an ultra-efficient and hydrogen enabled integrated airframe for targeted ultra-efficient Short/Medium Range aircraft (SMR), i.e. 150-250 PAX and 1000-2000nm range. To enable climate-neutral flight, aircraft for short and medium-range distances have to rely on ultra-efficient thermal energy-based propulsion technologies using sustainable drop-in and non-drop-in fuels. Besides propulsion, the integration aspects of the fuel tanks and distribution system as well as sustainable materials for the fuselage, empennage are essential to meet an overarching climate-neutrality of the aviation sector. Green propulsion and fuel technologies will have a major impact on the full fuselage, including the rear fuselage, the empennage structure as well as cabin and cargo architecture in so far as the integration of storage and the integration of systems for the chosen energy source are concerned (H2, direct burn, fuel cell). Not only do the specific properties of hydrogen necessitate a re-consideration of typical aircraft configurations, requiring new design principles formulation and fundamental validation exercises, but they also raise a large number of important follow-on questions relating to hydrogen distribution under realistic operational constraints and safety aspects. The project will explore and exploit advanced production technologies for the integrated fuselage / empennage to reduce production waste and increase material and energy exploitation with Integrated Fuselage concept selected (maturity TRL3/4) until end of first phase in 2025. An anticipated route to TRL6 until end of the Clean Aviation programme in 2030 will ensure entry-into-service in 2035.
      78
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    Fortlaufende Anpassung von LKW Abfertigungszeitfenstern durch flEXIble Künstliche INtelliGenz; Teilvorhaben: Optimale Zeitfensterermittlung und Equipmentplannung
    Beim Güterumschlag zwischen einzelnen Transportmodi an Seehafen‐ und Hinterlandterminals lassen sich im Tagesverlauf oft hohe Belastungsschwankungen beobachten. In Folge dessen kann es zu Wartezeiten und Staus von Lkw sowie einer suboptimalen Auslastung des Umschlagsequipment kommen. Um die Ablaufgestaltung zu verbessern, führen immer mehr Terminals sogenannte Zeitfensterbuchungssysteme (ZFBS) zur Glättung der Lastspitzen ein. Erfahrungen aus der Praxis haben jedoch nach und nach gezeigt, dass sich mit einer starren Vergabe von Zeitfenstern kein Gesamtoptimum (optimale Auslastung des Terminals bei minimalen Wartezeiten der Lkw) erreichen lässt. Hier setzt das Vorhaben FLEXIKING an. Ziel ist es zu demonstrieren, wie ein kollaboratives und flexibles ZFBS den Interessen von Terminal und Speditionen gleichermaßen gerecht werden kann. Dabei wird über ein flexibles Zeitfensterbuchungssystem fortlaufend die Anzahl von Abfertigungsfenstern am Umschlagspunkt optimieren. Gleichzeitig soll eine verteilte künstliche Intelligenz es ermöglichen, dass eine kollaborative Anpassung zuvor gebuchter Zeitfenster erfolgen kann.
      82  8
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      30  11