scholarly journals ON A DESIGN WAVE SPECTRUM

1984 ◽  
Vol 1 (19) ◽  
pp. 25
Author(s):  
Paul C. Liu
Keyword(s):  
Great Lakes ◽  
Wave Height ◽  
Deep Water ◽  
Water Wave ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Wave Spectra ◽  
Practical Applications ◽  
Average Wave ◽  
Deep Water Wave

We propose the use of a generalized representation for acquiring a design wave spectrum. The generalized form, free from any predetermined coefficients and exponents, requires only significant wave height and average wave period as input for practical applications. The usefulness of this representation has been demonstrated with over 2000 measured deep-water wave spectra recorded from NOMAD buoys in the Great Lakes during 1981.

scholarly journals Wave spectral shapes in the coastal waters based on measured data off Karwar, west coast of India

2017 ◽  
Author(s):  
M. Anjali Nair ◽  
V. Sanil Kumar
Keyword(s):  
Wave Height ◽  
Coastal Waters ◽  
High Frequency ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Temporal Variations ◽  
Wave Spectra ◽  
Monsoon Period ◽  
Significant Wave ◽  
Spectral Shapes

Abstract. Understanding of the wave spectral shapes is of primary importance for the design of marine facilities. In this paper, the wave spectra collected from January 2011 to December 2015 in the coastal waters are examined to know the temporal variations in the wave spectral shape. For 31.15 % of the time, peak frequency is between 0.08 and 0.10 Hz and the significant wave height is also relatively high (~ 1.55 m) for waves in this class. The slope of the high-frequency tail of the monthly average wave spectra is high during the Indian summer monsoon period (June–September) compared to other months and it increases with increase in significant wave height. There is no much interannual variation in slope for swell dominated spectra during the monsoon, while in the non-monsoon period when wind-seas have much influence, the slope varies significantly. Since the high-frequency slope of the wave spectrum is within the range 3–4 during the monsoon period, Donelan spectrum shows better fit for the wave spectra in monsoon months compared to other months.

Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method

2011 ◽  
Author(s):  
Maziar Golestani ◽  
Mostafa Zeinoddini
Keyword(s):  
Support Vector Regression ◽  
Wave Height ◽  
Significant Wave Height ◽  
Rational Design ◽  
Wave Spectrum ◽  
Support Vector ◽  
Wave Spectra ◽  
Significant Wave ◽  
Wave Parameters ◽  
Data Gaps

Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of the model to fill data gaps was tested using different approaches. Concurrent wind and wave records (standard meteorological and spectral density data) from 4 stations in 2003, 2007, 2008 and 2009 were used both for the training the SVR system and its verification. The choice of these four locations facilitated the comparison of model performances in different geographical areas. The SVR model was then used to obtain predictions for the wave spectra and also time series of wave parameters (separately for each station) such as its Hs and Tp from spectra and wind records. New approach was used to predict wave spectra comparing to similar studies. Reasonably well correlation was found between the predicted and measured wave parameters. The SVR model was first trained and tested using various methods for selecting training data. Also different values for SVM parameters (e.g. tolerance of termination criterion, cost, and gamma in kernel function) were tested. The best possible results were obtained using a Unix shell script (in Linux) which automatically implements different values for different input parameters and finds the best regression by calculating statistical scores like correlation of coefficient, RMSE, bias and scatter index. Finally for a better understanding of the results, Quantile-Quantile plots were produced. The results show that SVR can be successfully used for prediction of Hs and wave spectrum out of a series of wind and spectral wave parameters inputs. Also it was noticed that SVR is an efficient tool to be used when data gaps are present in the data.

scholarly journals ON SPECTRUM CALIBRATION FOR NEARSHORE WAVE TRANSFORMATION

2018 ◽  
pp. 50
Author(s):  
Zhong Peng ◽  
Jill Bradon
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Wave Model ◽  
Accurate Solution ◽  
Least Square ◽  
Wave Transformation ◽  
Wave Spectra ◽  
Wave Modelling ◽  
Spectrum Calibration

A ‘shape-focus’ method is proposed for wave spectrum calibration. In this method calibration factors at each frequency bin are derived from measured and modelled data using a least square error approach. Modelled wave spectra are calibrated using the shape-focus method and then applied to a SWAN wave model to simulate nearshore wave transformation. Nearshore wave spectra are then compared with measurements. Results show that spectrum calibration with the shape-focus method improves wave data accuracy significantly for both significant wave height and mean wave periods, thus provides a more accurate solution to calibrating wave spectra for nearshore wave modelling.

scholarly journals PARAMETRIC WAVE-BREAKING ON STEEP REEFS

2011 ◽  
Vol 1 (32) ◽  
pp. 16 ◽  
Author(s):  
Shih-Feng Su ◽  
Alex Sheremet ◽  
Jane McKee Smith
Keyword(s):  
Deep Water ◽  
Water Wave ◽  
Wave Spectrum ◽  
Nonlinearity Parameter ◽  
Wave Steepness ◽  
Mild Slope Equation ◽  
Empirical Relations ◽  
Deep Water Wave ◽  
The Relationship ◽  
Selection Of

A numerical model based on a nonlinear mild-slope equation, and modified to account for wave dissipation due to breaking is applied to investigate the transformation of the wave spectrum over a fringing reef. The three parameters (γ, B, F) of the breaking model are calibrated for the best fit between the spectral shapes observed and modeled using an inverse modeling approach. The relationship between optimal values for γ and B derived from numerical simulations and other parameters characterizing wave and slope conditions (e.g., deep-water wave steepness, wave dispersivity, nonlinearity parameter) are investigated with the goal of formulating guidelines for the selection of adequate values. The results of this study disagree significantly with previously-proposed empirical relations between γ and the deep-water wave steepness, but show good agreement with empirical relations relating γ to other parameters. The breaking intensity parameter B shows a largely linear dependency on the nonlinearity parameter.

scholarly journals MODEL TESTS ON THE BELATIONSHIP BETWEEN DEEP--WATEB WAVE CHARACTERISTICS AND LONGSHORE CURRENTS

1964 ◽  
Vol 1 (9) ◽  
pp. 11 ◽  
Author(s):  
Arthur Brebner ◽  
J.W. Kamphus
Keyword(s):  
Wave Height ◽  
Deep Water ◽  
Water Wave ◽  
Model Tests ◽  
Shore Line ◽  
Beach Slope ◽  
Wave Characteristics ◽  
Longshore Currents ◽  
Deep Water Wave

It has Long been recognized that the movement of Littoral material takes place, in the main, in the onshore regions of a beach where breaking of waves occurs. Waves whose crests in deep water make an angLe o(a with the shore Line, and which break at an angLe C*© , are the mam source of energy for the generation of the forces which manifest themselves in Long-shore currents and the resulting LittoraL transport. This littoraL materiaL is put into motion before, during and after breaking but it is extremeLy difficult to separate the effects of the forces and currents in these three zones. In what foLlows the authors have attempted to measure the intensity of the current around the breaking zone in a highLy ideaLized beach model in which the shore Line is straight, has a constant beach sLope, 0, and is attacked by waves of constant deep-water wave-height, HQ, and period, T. During refraction and shoaLing the angLe of the wave-crests with the shore-Line is reduced from o

scholarly journals NEARSHORE WAVE SPECTRA MEASURED DURING HURRICANE DAVID

1982 ◽  
Vol 1 (18) ◽  
pp. 5
Author(s):  
Michel K. Ochi ◽  
Ming-Hsing Chiu
Keyword(s):  
United States ◽  
Wave Height ◽  
Deep Water ◽  
Significant Wave Height ◽  
The United States ◽  
Wave Spectra ◽  
Significant Wave ◽  
Stages Of Growth

This paper presents the results of an analysis carried out on wave spectra measured at three nearshore sites along the United States Florida coast when Hurricane DAVID passed these sites in 1979. Included are (a) the variability of the shapes of wave spectra during the stages of growth and decay of the hurricane-generated seas, (b) the presentation of spectra according to various spectral formulations, and (c) a comparison between spectra measured at the coastal sites and those measured in deep water for the same severity (significant wave height) of hurricane-generated seas.

Sea State Estimation Based on Ship Motions Measurements and Data Fusion

2013 ◽  
Author(s):  
Céline Drouet ◽  
Nicolas Cellier ◽  
Jérémie Raymond ◽  
Denis Martigny
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Ship Motions ◽  
Sea State ◽  
Significant Wave ◽  
X Band

In-service monitoring can help to increase safety of ships especially regarding the fatigue assessment. For this purpose, it is compulsory to know the environmental conditions encountered: wind, but also the full directional wave spectrum. During the EU TULCS project, a full scale measurements campaign has been conducted onboard the CMA-CGM 13200 TEU container ship Rigoletto. She has been instrumented to measure deformation of the ship as well as the sea state encountered during its trip. This paper will focus on the sea state estimation. Three systems have been installed to estimate the sea state encountered by the Rigoletto: An X-band radar from Ocean Waves with WAMOS® system and two altimetric wave radars from RADAC®. Nevertheless, the measured significant wave height can be disturbed by several external elements like bow waves, sprays, sea surface ripples, etc… Furthermore, ship motions are also measured and can provide another estimation of the significant wave height using a specific algorithm developed by DCNS Research for the TULCS project. As all those estimations are inherently different, it is necessary to make a fusion of those data to provide a single estimation (“best estimate”) of the significant wave height. This paper will present the data fusion process developed for TULCS and show some first validation results.

Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach

2016 ◽  
Author(s):  
Laks Raghupathi ◽  
David Randell ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Structural Reliability ◽  
Structural Response ◽  
Environmental Parameters ◽  
Joint Estimation ◽  
Current Profile ◽  
Practical Applications ◽  
Significant Wave ◽  
Northern North Sea

Understanding the interaction of ocean environments with fixed and floating structures is critical to the design of offshore and coastal facilities. Structural response to environmental loading is typically the combined effect of multiple environmental parameters over a period of time. Knowledge of the tails of marginal and joint distributions of these parameters (e.g. storm peak significant wave height and associated current) as a function of covariates (e.g. dominant wave and current directions) is central to the estimation of extreme structural response, and hence of structural reliability and safety. In this paper, we present a framework for the joint estimation of multivariate extremal dependencies with multi-dimensional covariates. We demonstrate proof of principle with a synthetic bi-variate example with two covariates quantified by rigorous uncertainty analysis. We further substantiate it using two practical applications (associated current given significant wave height for northern North Sea and joint current profile for offshore Brazil locations). Further applications include the estimation of associated criteria for response-based design (e.g., TP given HS), extreme current profiles with depth for mooring and riser loading, weathervaning systems with non-stationary effects for the design of FLNG/FPSO installations, etc.

scholarly journals A Simple Method for Retrieving Significant Wave Height from Dopplerized X-Band Radar

2016 ◽  
Author(s):  
Ruben Carrasco ◽  
Michael Streßer ◽  
Jochen Horstmann
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Doppler Velocity ◽  
Modulation Transfer ◽  
Simple Method ◽  
Backscatter Intensity ◽  
Significant Wave ◽  
X Band ◽  
Variance Spectrum

Abstract. Retrieving spectral wave parameters such as the peak wave direction and wave period from marine radar backscatter intensity is very well developed. However, the retrieval of significant wave height is difficult because the radar image spectrum (a backscatter intensity variance spectrum) has to be transferred to a wave spectrum (a surface elevation variance spectrum) using a modulation transfer function (MTF) which requires extensive calibration for each individual radar setup. In contrast to the backscatter intensity, the Doppler velocity measured by a coherent radar is induced by the radial velocity of the surface scattering and its periodic component is mainly the contribution of surface waves. Therefore, the variance of the Doppler velocity can be utilized to retrieve the significant wave height. Analysing approximately 100 days of Doppler velocity measurements of a coherent on receive radar operating at X-band with vertical polarization in transmit and receive, a simple relation was derived and validated to retrieve significant wave heights. Comparison to wave measurements of a wave rider buoy as well as an acoustic wave and current profiler resulted in a root mean square error of 0.24 m with a bias of 0.08 m. Furthermore, the different sources of error are discussed and investigated.

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