Entwicklungszentrum für Schiffstechnik und Transportsysteme

Pulsed electric field technology possesses a high potential and a bright future in food processing to inactivate microorganisms and reduce enzymatic activity. Processed food shows a higher retention of health-related compounds and an extension of the shelf-life compared to conventional pasteurization methods. This technology is gradually moving from the laboratory and pilot-plant scale to the commercial scale. In the current review, we focus on the way existing knowledge on mathematical modeling and computational approaches is structured, connected, and interpreted across scales. We start with the electroporation models, progressing from those that are derived from simple physical and chemical considerations to those that are based on more complex probabilistic approaches. They attempt to predict how electric pulses create pores in cell membranes and form the basis of kinetic inactivation models. Subsequently, we examine the most common kinetic models of microorganism inactivation, from first-order models to models based on random and probabilistic considerations. We then review the works carried out on the numerical simulations of the electric field in a continuous PEF chamber and the works related to coupled simulations of the electric, fluid flow, temperature, and inactivation kinetic field. Finally, we conclude the manuscript with a section dedicated to the current applications of the PEF process to demonstrate its effectiveness.

The innovation of an intermodal bulk cargo transport service on the Danube addresses the gradual shift in the IWT market towards smaller quantities and more demand-oriented supplies to the customer. The design of the new transport service requires a decision on the precise service configuration with concept options between direct haulage, single-stop services and various liner services. For this purpose, agricultural production volumes and the infrastructural and nautical conditions along the Danube corridor have been used for the set-up of different service configuration variants. With the help of a dedicated logistics simulation study, the different service configurations have been examined and compared with one another with respect to a predetermined set of key performance indicators. The results of the study define the upscaling strategy following the initial deployment at a smaller scale, i. e., on a particular transport relation between a consignor and a consignee. The different extension options have been examined, compared with one another, and interpreted regarding their applicability. The results of the simulation study function as a guidance to decision-makers how the system behaves in different configurations.

Abstract Automation in inland waterway transportation is progressing, with an increasing number of vessel-operational functions being automated and pertaining operations changed. Whereas changes to the technical configuration and the immediate processes are identified with little effort, the impact of the introduction of a technological innovation like automated IWT to the entirety of transport and logistics process flow organisation is difficult to determine. With the help of a structured methodology to identify and assess the process-related changes and implication induced by the technological innovation, the precise changes in process flows, stakeholder involvement and role assignments, and system involvement can be identified. This article presents a multi-step approach to this endeavour and substantiates the proposed methodology with an application example. The methodology allows an early assessment of the extent of change to the entire process organisation, the early detection of constellations adversary to adoption by all required parties, and the early identification of the alignment needs between parties. The identified changes can be used as a reference for the subsequent performance evaluation following the introduction of the technological innovation.

ABSTRACT An analysis of the regulatory framework for inland navigation indicates that the present definition of Master is one of the major obstacles to the implementation of autonomous shipping on inland waterways in Europe. The paper dissects the definitions of Master as given in the ‘European Code for Inland Waterways’ and the German regulations ‘Binnenschifffahrtsstraßen-Ordnung’ and identifies the obstacles to unmanned and/or remotely controlled shipping created by these definitions. The identified obstacles are also analysed within the context of the vessel automation taxonomy of the Central Commission for the Navigation of the Rhine. Consequently, the recommendations are given on how to modify the definition of Master so as to include all levels of autonomy and account for the implications of the current as well as future technological developments. The main hypothesis of the paper is that the Master should not be ‘removed’ from an autonomous vessel but redefined instead.

Abstract The maritime trade and activities in regions with ice-covered waters such as the Arctic, Antarctica, the Baltic Sea, the Caspian Sea and the Great Lakes are expected to experience growth in the near future, mainly influenced by factors like: increased dry bulk and cargo freight in Arctic regions, rising tourism in Arctic and Antarctic waters, and increased exploitation and transport of oil and gas in Arctic areas. Most importantly, global warming — particularly in the North Pole — is opening new navigation routes through ice melting, affecting susceptible ecosystems and making previously inaccessible areas more navigable [1]. This highlights the need for a deeper understanding of Ship-Ice Interaction (SII) and manoeuvring performance in ice-infested waters. For a vessel advancing in ice, the primary focus pertains to the prediction of the ice resistance; however, the transverse force and turning moment are also of significance contemplating the manoeuvrability aspect whilst in turning circumstance. This paper focuses on developing a numerical model to simulate SII and ship manoeuvres in level ice. The model integrates existing semi-empirical formulas for calculating ice-pertinent forces and moments, and a coefficient-based model (by the Taylor series expansion) for hydrodynamic forces. Key findings indicate the Lindqvist method’s effectiveness in predicting ice resistance, and confirm the reliability of the turning circle manoeuvre (in open water) with the experimental data of Wolff [2]. Nonetheless, to improve the accuracy of the turning circle manoeuvre in ice, it is imperative to have access to all the characteristics of the model tests and ice distribution in the ice tank.

Physics-related and model-based vessel trajectory prediction is highly accurate but requires specific knowledge of the vessel under consideration which is not always practical. Machine learning-based trajectory prediction models do not require expert knowledge, but rely on the implicit knowledge extracted from massive amounts of data. Several deep learning (DL) methods for vessel trajectory prediction have recently been suggested. The DL models developed typically only process information about the (dis)location of vessels defined with respect to a global reference system. In the context of inland navigation, this can be problematic, since without knowledge of the limited navigable space, irrealistic trajectories are likely to be determined. If spatial constraintes are introduced, e.g., by implementing an additional submodule to process map data, however, overall complexity increases. Instead of processing the vessel displacement information on the one hand and the spatial information on the other hand, the paper proposes the merging of both information. Here, fairway-related and navigation-related displacement information are used directly. In this way, the previously proposed context-sensitive Classification Transformer (CSCT) shows an improved spatial awareness. Additionally, the CSCT is adapted to assess the model uncertainty by enabling dropout during inference. This approach is trained on different inland waterways to analyze its generalizability. As the improved CSCT obtains lower prediction errors and enables to estimate the trustworthiness of each prediction, it is more suitable for safety-critical applications in inland navigation than previously developed models.