Comparison of two statistical wave models for fatigue and fracture analysis of ship structures
Journal article, 2019

Ocean crossing ship structures are continuously suffering from wave loads when sailing at sea. The wave loads cause large variation of structural stresses, leading to fatigue accumulation in ship structures. For the fatigue life prediction of ship structures, it is important to obtain both the long-term distribution and the time history of wave-induced loads. An essential step is to get reliable wave statistics and accurate description of the stochastic nature of sea state along a ship’s sailing routes during her service time. Generally, the wave statistics are pro- vided by the classification societies as a joint probability of significant wave height and mean wave period, also known as wave scatter diagram. In addition, different statistical wave models have been developed to describe wave environments along arbitrary shipping routes based on different data sources, e.g., hindcast data, satellite measurements, buoys, etc. In this paper, two statistical wave models based on hindcast data and satellite wave measurements are briefly introduced and compared with the wave measurements carried out by onboard radar. Both of the wave models are then used to generate the wave environments along given shipping routes. The effectiveness of the wave models is demonstrated by comparing the stochastic nature and the statistical char- acteristics of simulated sea state histories with those of the source oceanographic data. Finally, an application of the wave model to the fatigue assessment is presented.

Significant wave height

Mean wave period

Spatio-temporal wave model

Ship fatigue design

Wave statistics

Storm model

Author

Luis De Gracia

Osaka University

Helong Wang

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Naoki Osawa

Osaka University

Igor Rychlik

Chalmers, Mathematical Sciences

Gaute Storhaug

Osaka University

Ocean Engineering

0029-8018 (ISSN)

Vol. Vol.187 106161

Explore innovative solutions for arctic shipping

The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), 2017-05-01 -- 2020-06-30.

Spatio-temporal modelling of MetOcean environment for safe and energy efficient maritime transport

EU H2020 - Marie Skłodowska-Curie Individual Fellowship (IF), 2018-09-20 -- 2019-10-30.

VGR MoRE2020, 2018-09-20 -- 2019-10-30.

EU H2020 - Marie Skłodowska-Curie Individual Fellowship (IF), 2018-09-20 -- 2020-12-31.

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Roots

Basic sciences

Subject Categories

Vehicle Engineering

Oceanography, Hydrology, Water Resources

Probability Theory and Statistics

DOI

10.1016/j.oceaneng.2019.106161

More information

Latest update

10/22/2019