Prediction of ice resistance of icebreaker during continuous icebreaking

Objective In order to accurately assess the ice resistance experienced by an icebreaker during continuous icebreaking when sailing in a level ice zone, and understand the characteristics of different prediction methods, this paper uses the empirical formula method, numerical simulation method and ship model test method to evaluate an icebreaker's continuous sailing in smooth ice and predict the ice resistance.

Method In this paper, the nonlinear finite element software DYNA is used to construct ice numerical simulation models based on the traditional finite element and cohesive element methods respectively, and simulations are performed of the bending fracture and interaction process that occurs when level ice interacts with an icebreaker. At the same time, using the empirical formula method, three different empirical formulas are used to calculate the ice resistance, and a sensitivity analysis of the parameters affecting the prediction results of the empirical formula method is also carried out.

Results The study finds that ice resistance shows an upward trend with the increase in speed, ice thickness and bending strength. Among them, ice thickness has the greatest influence on ice resistance. Among the three empirical formulas, the prediction results of the Lindqvist formula are closer to the ship model test results, while those of the Vance and Lewis formulas are more conservative. The traditional finite element and cohesive element methods obtain more accurate ice resistance prediction results when the thickness is small. When the thickness is large, the error is about 25%. In case of small ice thickness and high speed, the ice resistance value predicted by the cohesive element method is more accurate than that of the traditional finite element method, and the accuracy error is within 10% compared with the ship model test results.

Conclusion In the actual ice resistance prediction, the empirical formula method and numerical simulation method can be combined to take into account the accuracy and efficiency of the prediction results.