A probabilistic method for long-term estimation of ice loads on ship hull. (November 2021)

• Record Type: Journal Article
• Authors: Li, Fang; Suominen, Mikko; Lu, Liangliang; Kujala, Pentti; Taylor, Rocky
• Is Part Of: Structural safety. Volume 93(2021)
• Publication Date: 2021-11
• Subjects: Ice load -- Long-term estimation -- Probabilistic method -- Event Maximum Method -- Full-scale measurement -- Ice-going ships; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2021.102130
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: A probabilistic method based on Event Maximum Method is proposed for long-term estimation of ice loads on ship hull. The method includes ice concentration as another ice condition parameter in addition to thickness. For parameter estimation, a maximum likelihood approach and a probability paper approach are proposed. Six years data from Antarctic voyages are used to validate the method. Abstract: Ships navigating in ice-infested regions need strengthened hull to resist the loads arising from the interactions with ice. Correct estimation of the maximum ice loads a ship may encounter during its lifetime is of vital importance for the design of ship structures. Due to the stochastic nature of ice properties and interaction processes, probabilistic approaches are useful to make long-term estimations of local ice loads on the hull. The Event Maximum Method (EMM) is an existing probabilistic approach for the long-term estimation of ice loads on the hull. This paper aims to extend the current EMM, first by introducing a model for the intercept of the linear regression line on the abscissa in order to quantify this value. Moreover, ice concentration is considered in the extended method as the second ice condition parameter in addition to thickness. The proposed method is applied to the full-scale measurement of the ship S.A. Agulhas II using the data obtained from the 2018/19 Antarctic voyage. The obtained model is then validated against six-year measurement data from 2013 to 2019, which shows reasonable similarity.