Repeatability of ice-tank tests with broken ice

Highlights

• The repeatability of ice tank tests in broken ice is assessed by analysis and numerical modelling of two test campaigns.

• The initial ice floe positions can have a large influence on the results of ice tank tests with broken ice.

• Single interaction events can influence the ice load statistics for a large portion of the interaction length.

• The floe concentration ahead of the structure should be taken into account when analysing broken ice test results.

Abstract

This study investigates the repeatability of ice-tank tests with broken ice. Ice-tank test campaigns normally do not perform multiple repetitions of tests with the same initial conditions. Therefore, the repeatability of ice-tank tests with broken ice is not well understood. Data from two test campaigns are analysed. The first test campaign studied the interaction between a 4-legged structure with a vertical waterline and several broken and intact level ice conditions. In the second test campaign, a ship hull geometry was tested. We analyse selected test cases from each test campaign. The ice-tank tests are reproduced using a 3-D discrete element method (DEM) model. Each analysed test case is simulated 20 times. The only difference between each simulation is the initial position of the ice floes. The numerical simulation results show that changes in the initial floe positions can cause large changes in the statistical properties of the ice load. Often, a single random interaction event can be identified that is responsible for the change in the results. Such interaction events can cause additional floe accumulation ahead of the structure, thereby influencing the load statistics for a large portion of the interaction length. The observed events occur both in the numerical simulations and in the physical ice-tank tests. This result indicates that ice-tank tests with broken ice have a poor repeatability; a change in an uncontrolled condition, such as the exact initial floe positions, can lead to a large variation in the experimental results.