scholarly journals Estimating Maximum Significant Wave Height and Dominant Wave Period inside Tropical Cyclones

2018 ◽  
Vol 33 (4) ◽  
pp. 955-966 ◽  
Author(s):  
Paul A. Hwang ◽  
Edward J. Walsh
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Period ◽  
Maximum Wind Speed ◽  
Power Functions ◽  
Significant Wave ◽  
Maximum Wind ◽  
Dominant Wave

Abstract Making use of the fetch- and duration-limited nature of wind-wave growth inside tropical cyclones, an algorithm is developed to estimate the maximum significant wave height and dominant wave period of surface waves generated by tropical cyclone wind fields. The results of the maximum significant wave height and dominant wave period are further approximated by simple power functions of the maximum wind speed. The exponents of the power functions are almost constant, and the proportionality coefficients can be approximated by second-order polynomial functions of the radius of maximum wind speed (RMW). The predicted maximum values agree well with results derived from simultaneous wind and wave measurements obtained during 11 hurricane reconnaissance and research missions in six hurricanes.

Development of a new significant wave height and dominant wave period parameterization scheme

2017 ◽  
Vol 135 ◽  
pp. 170-182 ◽  
Author(s):  
Chendi Wang ◽  
Jianfang Fei ◽  
Juli Ding ◽  
Ruiqing Hu ◽  
Xiaogang Huang ◽  
...  
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Period ◽  
Parameterization Scheme ◽  
Significant Wave ◽  
Dominant Wave

scholarly journals ANFIS and ANN models for the estimation of wind and wave-induced current velocities at Joeutsu-Ogata coast

2015 ◽  
Vol 18 (2) ◽  
pp. 371-391 ◽  
Author(s):  
Morteza Zanganeh ◽  
Abbas Yeganeh-Bakhtiary ◽  
Takao Yamashita
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Water Depth ◽  
Incident Wave ◽  
Wind Direction ◽  
Significant Wave Height ◽  
Wave Period ◽  
Inference System ◽  
Significant Wave ◽  
Wave Induced

In this study, the adaptive network-based fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to estimate the wind- and wave-induced coastal current velocities. The collected data at the Joeutsu-Ogata coast of the Japan Sea were used to develop the models. In the models, significant wave height, wave period, wind direction, water depth, incident wave angle, and wind speed were considered as the input variables; and longshore and cross-shore current velocities as the output variables. The comparison of the models showed that the ANN model outperforms the ANFIS model. In addition, evaluation of the models versus the multiple linear regression and multiple nonlinear regression with power functions models indicated their acceptable accuracy. A sensitivity test proved the stronger effects of wind speed and wind direction on longshore current velocities. In addition, this test showed great effects of significant wave height on cross-shore currents' velocities. It was concluded that the angle of incident wave, water depth, and significant wave period had weaker influences on the velocity of coastal currents.

scholarly journals Physical Modelling of the Effect on the Wave Field of the WaveCat Wave Energy Converter

2021 ◽  
Vol 9 (3) ◽  
pp. 309
Author(s):  
James Allen ◽  
Gregorio Iglesias ◽  
Deborah Greaves ◽  
Jon Miles
Keyword(s):  
Wave Height ◽  
Wave Energy ◽  
Significant Wave Height ◽  
Wedge Angle ◽  
Wave Energy Converter ◽  
Wave Period ◽  
Surface Elevation ◽  
Energy Converter ◽  
Significant Wave ◽  
Model Scale

The WaveCat is a moored Wave Energy Converter design which uses wave overtopping discharge into a variable v-shaped hull, to generate electricity through low head turbines. Physical model tests of WaveCat WEC were carried out to determine the device reflection, transmission, absorption and capture coefficients based on selected wave conditions. The model scale was 1:30, with hulls of 3 m in length, 0.4 m in height and a freeboard of 0.2 m. Wave gauges monitored the surface elevation at discrete points around the experimental area, and level sensors and flowmeters recorded the amount of water captured and released by the model. Random waves of significant wave height between 0.03 m and 0.12 m and peak wave periods of 0.91 s to 2.37 s at model scale were tested. The wedge angle of the device was set to 60°. A reflection analysis was carried out using a revised three probe method and spectral analysis of the surface elevation to determine the incident, reflected and transmitted energy. The results show that the reflection coefficient is highest (0.79) at low significant wave height and low peak wave period, the transmission coefficient is highest (0.98) at low significant wave height and high peak wave period, and absorption coefficient is highest (0.78) when significant wave height is high and peak wave period is low. The model also shows the highest Capture Width Ratio (0.015) at wavelengths on the order of model length. The results have particular implications for wave energy conversion prediction potential using this design of device.

Comparison of Bivariate Models of the Distribution of Significant Wave Height and Peak Wave Period

2007 ◽  
Author(s):  
Catarina S. Soares ◽  
C. Guedes Soares
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Bivariate Normal Distribution ◽  
Wave Period ◽  
Peak Period ◽  
Data Set ◽  
Significant Wave ◽  
The North ◽  
Bivariate Models ◽  
The North Sea

This paper presents the results of a comparison of the fit of three bivariate models to a set of 14 years of significant wave height and peak wave period data from the North Sea. One of the methods defines the joint distribution from a marginal distribution of significant wave height and a set of distributions of peak period conditional on significant wave height. Other method applies the Plackett model to the data and the third one applies the Box-Cox transformation to the data in order to make it approximately normal and then fits a bivariate normal distribution to the transformed data set. It is shown that all methods provide a good fit but each one have its own strengths and weaknesses, being the choice dependent on the data available and applications in mind.

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