Ulrik Jørgensen's scientific contributions

This paper explores the feasibility of existing automated mooring systems for short-sea container ships and deep-sea bulk ships. The systems in question include vacuum-based, magnetic-based, and robotic arm-based solutions. By evaluating input from users and system suppliers, the study considers the performance of these systems in terms of safety, operation time, efficiency, sustainability, and emissions reduction. The paper also identifies potential challenges associated with the use of automated mooring systems in these contexts. Overall, this study provides valuable insights for stakeholders seeking to enhance the efficiency and safety of mooring operations in the shipping industry.

This paper presents how autonomous tests can be documented and why this is important. A test area in Norway, more specifically the Trondheimsfjorden Test Area for Autonomous Ships, is used as a pilot to conduct tests with autonomous vessels and for demonstrating the procedure of documenting results. There are typically three stages in such a documentation process; 1) To register and inform about a planned test on the fjord, 2) To inform about ongoing tests and to document test results by collecting data from the vessel and from the sensor infrastructure, 3) To show historical tests and be able to do analytics or conduct learning from previous tests. The Trondheimsfjorden Test Area has been instrumented with communication and navigation infrastructure, a control centre for control and monitor of the install infrastructure and for remote operation of a ship, and a data centre for planning autonomous tests, storing data, and for sharing of test results. By documenting test results in a standardized format, this can be used to verify new technology and solutions, share knowledge and experiences, and for documentation procedures and guidelines used for the purpose. A demonstration held in The Trondheimsfjorden Test Area showed the importance of streamlining the process of conducting autonomous test and documenting them in a standardized format. This work is based on the results from the research project NAVISP-EL3-005 “Trondheimsfjorden Test Area for Autonomous Ships”. The Navigation Innovation and Support Programme (NAVISP) is the programme of the European Space Agency to support the competitiveness of the European industry in the wide field of positioning, navigation and timing while supporting member states in enhancing national objectives and capabilities in the sector.

This paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. The first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control.

A weather routing system for a ferry in Norway has been developed. In this paper three different models for estimating the energy consumption have been evaluated. The three energy models include two data-driven models, i.e., a linear regression model and a hybrid model, and a simulation-based energy model. The hybrid energy model performed best with the highest accuracy and at the lowest run time. Moreover, using the hybrid energy model, the weather routing system has been tested and evaluated. The results show typical energy savings up to 1,5% are achieved.

This paper presents a method for energy efficient routing of a symmetrical electrical car ferry in Norway. Historical and operational data from the ferry and environmental data (wind, current, and waves) have been used to develop a machine learning model that predicts the energy consumption. Data from more than 2000 trips have been used for training, validation, and testing of the model. By combining weather forecast and the established energy prediction model it is possible to propose more energy efficient route during the transit phase. Energy saving up to 3% are achieved on a selection of representative routes.