A Spatio-Temporal Statistical Model to Simulate Long-Term Wave Environments along Ship Routes
Paper in proceedings, 2019

Any maritime related research and innovation activities strongly rely on accurate description of meteorological and oceanographical (MetOcean) conditions where ships or marine structures will be encountered. For example, in order to determine a ship’s fatigue life or extreme loading conditions during a ship’s service life, it is essential to get reliable long-term wave statistics along the ship’s sailing areas. A ship’s encountered wave environments and operational conditions often differ from that assumed in the design rules. The estimated fatigue life according to classification rules may differ significantly from the ship’s actual fatigue life. It will lead to fatigue cracks initiated much earlier than expected. To get the encountered wave environments in the future sailing. A straightforward way is to use the wave scatter diagram given by classification societies. However, it has been reported that due to the current high technical help of ship navigation, such as weather routing systems, a ship can easily avoid the upcoming storms by optimizing the ship routings. It leads to the distribution of encountered wave environments much milder than that given by class guidelines. Alternatively, large database of metocean parameters is available by public from different measurement sources, e.g. satellites, hindcast, buoys etc. In order to make use of these large amounts of data for practical applications, much effort has been made to develop wave models, which can describe the wave environments encountered by a specific ship in a more efficient way. These models are built up based on different measurement sources. In this study, we will briefly present a spatio-temporal statistical wave model that describe the mean and covariance structure of ocean waves, which is represented by significant wave height Hs. The model is demonstrated to simulate Hs along ship routes recorded by a fleet of containerships sailing in the North Pacific Ocean. The basic statistics of Hs from the onboard observed in the noon report are compared with that from simulated Hs and that from ECMWF hindcast ERA5 dataset. Finally, some conclusions are drawn based on the demonstration study of the model.

containerships

Wave statistics

significant wave height

wave distribution

ship fatigue

Author

Wengang Mao

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Naoki Osawa

shengzheng wang

Shanghai Maritime University

Di Zhang

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

Vol. Vol.28 2019S-GS1-2

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

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

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

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

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

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Subject Categories

Meteorology and Atmospheric Sciences

Marine Engineering

Probability Theory and Statistics

Roots

Basic sciences

More information

Created

7/29/2019