Reconstruction of Wave Field around Taiwan Sea Area using Ship Motions of a Fleet
Date Issued
2009
Date
2009
Author(s)
Tsai, Cheng-Han
Abstract
In recent years, artificial neural network (ANN) has been applied on the field of ocean engineering and naval architecture. Most of these researches are used to do prediction in time domain, and the accuracy is acceptable for engineering applications. In the present study, the pattern recognition ability of ANN is applied to develop a model for evaluating wave characteristics basing on ship motions. In addition, ANN is also applied to reconstruct the spatial distribution of wave field around Taiwan sea area by using limited numbers of sampling wave data. In the present study, maybe 50~70 ships of the fleet of Taiwan Coast Guard are considered as the mobile agents of a sensor network for capturing the wave field around Taiwan sea area. By sensing the motion responses of each ship at the same time and putting them into ANN model, the significant wave height、wave period、incident angle and ship speed at the location of each ship can be evaluated. Then puting these scattered wave data (about 50~70 sets) into another ANN model, the spatial distribution of wave field around Taiwan sea area can be reconstructed. In this study, SWAN data of the TaiCOMS are applied for simulating the proposed ship-bone wave monitoring system and verifying the accuracy and feasibility of this system. MATLAB is used to develop the software to simulate the ship motions in short crested irregular waves, and Neural Solution is applied to develop the ANN models. ANN models have a good performance on evaluating wave characteristics basing on ship motions and reconstructing the spatial distribution of wave field around Taiwan sea area by using limited numbers of sampling wave data. The result of this research shows that the present ship-bone wave monitoring system is feasible with acceptable accuracy for engineering use.
Subjects
Artificial Neural Network
Ship Motion
Wave Field
Spatial Distribution
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R96525004-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):6f4e866a5cd9c7a6c1392dc2d30e2c64