Local Design Pressures for Structures in Ice: Analysis of Full-Scale Data

Abstract

The design of structures for ice conditions requires knowledge of local ice pressures to allow for appropriate levels of structural strengthening. Full-scale field data are keys to enhancing our understanding and modeling of ice behavior. Data collected during icebreaker ramming events represent an important source of information for use in design load estimation, and the evaluation of design methodologies. This paper examines several ship-ice interaction data sets using the ‘event-maximum’ method of local pressure analysis developed by and (1993, “Probabilistic Analysis of Local Ice Pressures,” ASME J. Offshore Mech. Arct. Eng., 115, pp. 83–89). In this method, the local pressure is obtained from a normalized curve, which contains two parameters α and x0. The parameter α is a function of the area, well represented by the curve α=CaD, where a is the local area of interest, and C and D are constants. The parameter x0 is assumed a constant for a given design scenario. An alternative approach, the up-crossing rate method, is presented in a companion paper (2009, “Estimation of Local Ice Pressure Using Up-Crossing Rate,” Proceedings of the OMAE 2009 , Honolulu, HI). Local pressure analysis results for data from the USCGS Polar Sea, CCGS Terry Fox, CCGS Louis St. Laurent, and Swedish Icebreaker Oden are presented. A discussion of panel exposure, event duration, and the effects of these factors on x0 is given. New design curves are included. For all data considered, the calculated values of α fall below the design curve. For the design, it is recommended that α is calculated using a C value based on the impact data collected under ice conditions similar to those for the design scenario; D may be treated as a constant having a value of −0.7. A design value of x0 may be determined based on the analysis of appropriate data sets. The treatment of exposure is described for data analysis and design. The effects of exposure must be removed during data analysis to provide a design curve based on single panel exposure. For the design, estimates from the design curves must be adjusted to properly reflect the design exposure.