Comparative Study on Two Statistical Wave Load Models and Their Application to Fatigue Assessment
Paper in proceedings, 2019

The shipbuilding industry has been under constant development. Nowadays, bigger and lighter ships are sailing worldwide shipping routes, and one of the crucial structural problems to be challenged is the fatigue damage to the hull structure. Ships structures should be safe and reliable against different kinds of fractures e.g., brittle fracture, fatigue failure, buckling, plastic collapse, among others. The corrosion, welding defects, material properties, wave load computations, etc, generally include large uncertainties in the ship fatigue analysis. In particular, large uncertainties are associated with the difference of the provided from actual wave environments encountered by a ship. These uncertainties may lead to fatigue cracks initiated much earlier than expected. The wave scatter diagrams, generally provided by the class rules guidelines, indicate the long-term probability distribution of waves at a specific sea area. Therefore, the actual wave environment encountered by individual ships may be not consistent with that provided by the class rule guidelines that are unlikely to consider operational conditions for individual ships. Therefore, a reliable description of the wave environments encountered by the ship during her service life is essential for the accurate fatigue assessment of ship structures. Statistical wave load models have been developed to model wave environments along ship arbitrary routes based on diverse wave data, e.g., onboard observations, satellite measurements, buoy data, and reanalysis data, etc. The wave information generated by the wave models could be useful for ship fatigue assessment and extreme loading predictions. This study presents two statistical wave models, one is based on wave scatter data extracted from reanalysis dataset along ship routes and the other is based on the Spatio-temporal correlation of wave from reanalysis and satellite measurements. A series of full-scale measurement data collected in a 2800TEU container carrier, when sailing in the North Atlantic during 2008, are used to validate the capabilities to describe the statistical properties of the wave conditions generated by the wave models. The stochastic nature and the statistical characteristics of sea states generated from the two wave models are compared with oceanographic information extracted from oceanographic reanalysis database data, actual onboard measurement and that provided by classification society guidelines for ship fatigue assessment. Finally, to present the applicability of the statistical wave models for the fatigue assessment of ship structures a simplified ship structural detail is selected. Based on the sea environments and stress histories generated by the two models, both the conventional high cycle fatigue life prediction and crack propagation method are demonstrated and compared with the results obtained from measured stress in the same containership.

Storm model

Spatio-temporal wave model

wave statistics

ship fatigue design

Author

Luis De Gracia

Helong Wang

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Naoki Osawa

Igor Rychlik

Chalmers, Mathematical Sciences

Annual meeting / The Japan Society of Naval Architects and Ocean Engineers

Vol. Vol.28 2019S-GS18-3

The Japan Society of Naval Architects and Ocean Engineers Annual meeting 2019
Nagasaki, Japan,

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

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

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

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

Explore innovative solutions for arctic shipping

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

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Subject Categories

Applied Mechanics

Geotechnical Engineering

Probability Theory and Statistics

Roots

Basic sciences

Learning and teaching

Pedagogical work

More information

Created

7/29/2019