Estimation and Analysis of Wave Spectrum Parameter using HeMOSU-2 Observation Data

In this study, wave spectrum data were calculated using the water surface elevation data observed at 5Hz intervals from the HeMOSU-2 meteorological tower installed on the west coast of Korea, and wave parameters were estimated using wave spectrum data. For all significant wave height ranges, the peak enhancement parameter (γ opt ) of the JONSWAP spectrum and the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated based on the observed spectrum, and the distribution of each parameter was confirmed. As a result of the analysis, the peak enhancement parameter (γ opt ) of the JONSWAP spectrum was calculated to be 1.27, which is very low compared to the previously proposed 3.3. And in the range of all significant wave heights, the distribution of the peak enhancement parameter (γ opt ) was shown as a combined distribution of probability mass function (PMF) and probability density function (PDF). In addition, the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated to be [0.245, β1.278], which are lower than the existing [0.300, -1.098], and the result of the linear correlation analysis between the two parameters was β = =3.86α.

Optimal Methods for Estimating the JONSWAP Spectrum Peak Enhancement Factor From Measured and Hindcast Wave Data

The specification of design wave spectra is based on spectral parameters estimated from available wave data. Measured data allow high-resolution estimates to be made, but they suffer from sampling variability and often do not contain a sufficient number of measurements in extreme sea states. Hindcast data can be used to examine spectral features associated with extreme sea states, but they can be limited by resolution and are dependent on how the physics are incorporated in the source terms of the wave model. We examine and compare the effects of estimating JONSWAP spectrum peak enhancement factor from measured and hindcast data for a range of sea states but particularly those with large significant wave heights. In the case of the measured data, we build on earlier work and review effects associated with different methods for processing measured time series data, including the effect of sampling frequency, record length, and smoothing. These are then contrasted with estimates made from typical hindcast data, including the effect of the spectral shape associated with different source terms—in particular the four-wave interaction term. Finally, we provide recommendations on how best to estimate the peak enhancement factor of the JONSWAP spectrum for design purposes.

Optimal Methods for Estimating the JONSWAP Spectrum Peak Enhancement Factor From Measured and Hindcast Wave Data

The specification of design wave spectra is based on spectral parameters estimated from available wave data. Measured data allow high-resolution estimates to be made, but they suffer from sampling variability and often do not contain a sufficient number of measurements in extreme sea states. Hindcast data can be used to examine spectral features associated with extreme sea states, but they can be limited by resolution and are dependent on how the physics are incorporated in the source terms of the wave model. We examine and compare the effects of estimating JONSWAP spectrum peak enhancement factor from measured and hindcast data for a range of sea states but particularly those with large significant wave heights. In the case of measured data, we build on earlier work and review effects associated with different methods for processing measured time series data, including the effect of sampling frequency, record length, and smoothing. These are then contrasted with estimates made from typical hindcast data, including the effect of the spectral shape associated with different source terms — in particular the four-wave interaction term. Finally, we provide recommendations on how best to estimate the peak enhancement factor of the JONSWAP spectrum for design purposes.

Motion and Dynamic Responses Approach for WindFloat Structures under Marine Loads

This paper presents performed Ansys numerical simulation on WindFloat structures. It contributes to the methodology of calculation based on CFD instruments to study the movements of the waves on semi-submersible stationary structures and highlights the importance of using RAO functions. RAO numerical approach developed can be applied to design semisubmersible WindFloat structures in order to fulfill essential requirements regarding operational safety and design. Numerical methodology was designed in accordance marine loads and the I.T.T.C. recommendations. The numerical RAO results for regular wave movements include many elements for the semi-submergible structure. Close results to open sea operation can be achieved by constant improvement in numerical simulation methodologies. The work opens the way for further hydrodynamic development on irregular waves for realistic hydrodynamic and structural response of the actual semi-submersibles at sea. The results demonstrate the accuracy of RAO function approach for specific WindFloat in Jonswap spectrum.

On the Uncertainties of Estimating JONSWAP Spectrum Peak Parameters

A spectral description of the wave spectrum is usually required in the design of offshore structures, and a generalised form of the JONSWAP spectrum is often used. The JONSWAP spectrum involves parameters that allow flexibility in the specification of the spectral peak, which is important for the response of both fixed and floating structures, but particularly for the floating structures. The peak of the wave spectrum is also important in nonlinear effects that for example contribute to the probability of a large crest occurring in a sea state time series realisation in a model basin, used to test a design platform. There has been a number of studies focused on the uncertainties of JONSWAP parameter estimates. We review these to establish an overview of the present understanding of the uncertainties, and we undertake further analyses to investigate the sensitivity of the uncertainties to the method of analysis and the types of data typically available for analysis, including both time series and spectral data.