This study focuses on the analysis of measured directional seas using a nonlinear model,
named Directional Hybrid Wave Model (DHWM). The model has the capability of
decomposing the directional wave field into its free wave components with different
frequency, amplitude, direction and initial phase based on three or more time series of
measured wave properties. With the information of free waves, the DHWM can predict
wave properties accurately up to the second order in wave steepness. In this study, the
DHWM is applied to the analyses of the data of Wave Crest Sensor Inter-comparison
Study (WACSIS). The consistency between the measurements collected by different
sensors in the WACSIS project was examined to ensure the data quality. The wave
characteristics at the locations of selected sensors were predicted in time domain and
were compared with those recorded at the same location. The degree of agreement
between the predictions and the related measurements is an indicator of the consistency
among different sensors.
To analyze the directional seas in the presence of strong current, the original DHWM
was extended to consider the Doppler effects of steady and uniform currents on the
directional wave field. The advantage of extended DHWM originates from the use of the
intrinsic frequency instead of the apparent frequency to determine the corresponding
wavenumber and transfer functions relating wave pressure and velocities to elevation. Furthermore, a new approach is proposed to render the accurate and consistent estimates
of the energy spreading parameter and mean wave direction of directional seas based on
a cosine-2s model. In this approach, a Maximum Likelihood Method (MLM) is
employed. Because it is more tolerant of errors in the estimated cross spectrum than a
Directional Fourier Transfer (DFT) used in the conventional approach, the proposed
approach is able to estimate the directional spreading parameters more accurately and
consistently, which is confirmed by applying the proposed and conventional approach,
respectively, to the time series generated by numerical simulation and recorded during
the WACSIS project.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4744 |
| Date | 25 April 2007 |
| Creators | Zhang, Shaosong |
| Contributors | Zhang, Jun |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1542351 bytes, electronic, application/pdf, born digital |
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