Numerical prediction of ship-ice interaction-a project presentation

• Published in: Volume 8: Polar and Arctic Sciences and Technology; Petroleum Technology
• Main Authors: Hahn, Malte, Dankowski, Hendrik, Ehlers, Sören, Erceg, Sandro, Rung, Thomas, Huisman, Michael, Sjöblom, Henrik, Leira, Bernt J., Chai, Wei
• Format: Conference Object
• Language: English
• Published: 2017
• Subjects: 600: Technik; 600; Arctic; Royce; Sea ice
• Online Access: http://hdl.handle.net/11420/3705

It is inevitable that commercial shipping and oil and gas resource exploitation activities in the Arctic will increase due to decreasing sea ice extent caused by global climate changes. Significantly more demanding and at the same time less well known environmental conditions create a need for reliable methods to assess icebreaking performance guaranteeing safe performance of the ships operating in this area subjected to various ice conditions. The classic approach of assessing ice-going performance, which combines class rules, experience and model tests, may not be applicable for the Arctic region in full. Furthermore, ship yards experience difficulties due to decreasing time frames and financial restrictions. Therefore this paper seeks to introduce a new development for a realistic and validated direct simulation approach for prediction of the hull load and icebreaking resistance that covers all aspects of the industrial design process and allows a more comprehensive analysis. The breaking model will provide a variable breaking pattern and is able to mimic the influence of the vessel speed and the environment on the ice loading and the predicted breaking length. In order to predict the extreme representative conditions to be simulated, a reverse extreme load prediction methodology is incorporated. An efficient, time dependent dynamic coupling between broken ice fragments, ice features, the 3D flow field and the ship's hull provides resistance values for performance calculations. The computational model will be validated against full-scale data and class rules using deterministic and probabilistic measures. This simulation approach is developed within international research collaboration between Pella Sietas, Rolls Royce Marine, TUHH and NTNU. An overview of the project together with the current status of the ongoing work including first results is presented.