Finite element based meta-modeling of ship-ice interaction at shoulder and midship areas for ship performance simulation

• Published in: Marine Structures
• Main Authors: Li, Fang, Kõrgesaar, Mihkel, Kujala, Pentti, Goerlandt, Floris
• Other Authors: Department of Mechanical Engineering, Marine Technology, Aalto-yliopisto, Aalto University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Ltd. 2020
• Subjects: Cross-scale modelling; Hip-ice interaction; Neural network; Ship performance in ice; XFEM; Arctic; Climate change
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/43381; https://doi.org/10.1016/j.marstruc.2020.102736

The climate change has made the transit through Arctic area more feasible, which demands reliable methods to evaluate ship performance. Ship performance in ice is a cross-scale problem, where the desired output such as ship speed lies in larger scale while the actual ship-ice interaction happens in smaller scale. Due to insufficient knowledge in ice mechanics and the demand for computational efficiency, existing approaches for modelling ship-ice interaction from ship performance perspective are mostly either (semi-) empirical, or simplified analytical, with reduced dimensions and extensively simplified mechanics. This paper presents a novel approach to model ship-ice interaction, which maintains the accuracy of the modelling with Finite Element Method (FEM) in ship-ice interaction scale, while being computationally very cheap, therefore is capable to be applied in ship scale simulations. The ice failure is firstly qualitatively investigated through full-scale and model-scale observations, as well as a numerical simulation with Extended Finite Element Method (XFEM). The model is then simplified and executed by Abaqus to automatically run a large database. A neural network is used to fit the results to get a simulation-free tool for ship-ice interaction calculation. Finally, the uncertainty in the results due to an important assumption is quantified. The results show that the obtained neural network fits the database with excellent performance. Therefore, it can be applied in ship scale simulations with improved accuracy compared to empirical or analytical approaches. Peer reviewed