Hurricane Wave Topography and Directional Wave Spectra in Near Real-Time

2001 ◽  
Author(s):  
Edward J. Walsh ◽  
C. W. Wright
Keyword(s):  
Real Time ◽  
Wave Spectra ◽  
Directional Wave

Real-Time Estimation of Directional Wave Spectra Using Non-Stationary Ship Motion Data

2009 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Stochastic Processes ◽  
Real Time ◽  
Time Estimation ◽  
Ship Motion ◽  
Wave Spectra ◽  
Time Step ◽  
Motion Data ◽  
Stationary Stochastic Processes ◽  
Real Time Estimation ◽  
Directional Wave

The Bayesian modeling procedure is modified for real-time estimation of directional wave spectra using non-stationary ship motion data. The assumption of stationary stochastic processes is applied to the seaway, but not to ship response because it also depends on ship maneuvers. Ship response is strongly affected by changes in the encounter angle and frequency of waves. Therefore, it is need to be a real-time algorithm that can deal with non-stationary stochastic processes and estimate the directional wave spectra. In the proposed algorithm, the iterative calculations of the non-linear equations were optimized and the convergence was not achieved at every time step, but was achieved gradually over several time steps. In order to examine the reliability of the proposed method, real-time estimation was conducted by using the data of onboard experiments. Comparisons between the results of the proposed algorithm and a wave monitoring radar system show good agreements.

Ship As a Wave Buoy: Estimating Full Directional Wave Spectra From In-Service Ship Motion Measurements Using Deep Learning

2020 ◽  
Author(s):  
T. P. Scholcz ◽  
B. Mak
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Accurate Estimation ◽  
Ship Motions ◽  
Wave Spectra ◽  
Operational Conditions ◽  
Directional Spectrum ◽  
The Neural Network ◽  
Directional Wave ◽  
Convolutional Encoding

Abstract The ocean wave directional spectrum is an important wave characteristic for maritime safety and navigation. Accurate estimation of directional spectra in real-time is a challenge. In this study we aim to reconstruct the directional spectra from ship motions using a deep convolutional encoding-decoding neural network. In-service measurements of ship motions and wave spectra from a WAMOS II wave scanning radar were used to train the neural network. The data was collected from a frigate type ship for a period of two years. We demonstrate that the deep convolutional encoding-decoding neural network is successful in predicting the directional spectra in real-time. At the same time, we conclude that more data is needed for a better prediction performance, including a more complete coverage of operational conditions.

Multi-peaked directional wave spectra based on extensive field measurement data in the Gulf of Oman

2021 ◽  
Vol 230 ◽  
pp. 109057
Author(s):  
Mohammad Adibzade ◽  
Mehdi Shafieefar ◽  
Hassan Akbari ◽  
Roozbeh Panahi
Keyword(s):  
Field Measurement ◽  
Measurement Data ◽  
Wave Spectra ◽  
Gulf Of Oman ◽  
Extensive Field ◽  
Directional Wave
Sign in / Sign up

Export Citation Format

Share Document