The rapid development of shipping in the Arctic region has increased the demand for special ice-class vessels, leading to the search for new and improved methods for modeling ship movement in ice conditions. Developing a mathematical model of ship-ice interaction is a complex task due to the challenges in simulating ice properties and there is no single universally accepted approach to ice modeling. However, numerous methods are available that can solve specific problems with sufficient accuracy. Despite their effectiveness, known numerical modeling methods for ship-ice interaction have significant limitations related to high computational costs and limited accuracy, highlighting the need for further improvement. The search for optimal combinations of different numerical methods and improving computational efficiency has become a key research area to improve the safety and cost efficiency of Arctic shipping. To conduct a comprehensive analysis of and systematize existing numerical modeling methods for ship-ice interaction and identify promising areas for their improvement to improve the efficiency of Arctic shipping. The review includes a comparative analysis of the advantages and disadvantages of each method (DEM, FEM, CEM, SPH, PD, and LBM) and an assessment of their applicability for solving specific ship-ice interaction modeling problems. The following aspects of method application were examined: computational efficiency, simulation accuracy, and optimal applications. The authors systematized the main numerical modeling methods for ship-ice interaction; identified common characteristics of all studied methods; determined key areas for improving existing approaches; developed recommendations for determining ice resistance, and made a list of software packages to implement modeling methods. The study highlights the need for further research in optimizing computational processes and improving the accuracy of ship-ice interaction modeling. This review provides recommendations for selecting a method to model ship movement in ice. However, all existing methods have their limitations, necessitating further development, including the development of combined approaches and the improvement of computational processes.
Berezhnaya et al. (2025) studied this question.