Directional Random Waves Propagation on Beaches

Grassa, José María

doi:10.1061/9780872627765.063

Cited by 5 publications

(5 citation statements)

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“…The wave directions, θ, span the range θ p − π 3 , θ p + π 3 , where θ p is the peak direction. Then, following Grassa [39], we calculate the wave amplitude components associated with each PWS seasonal spectrum such that:…”

Section: Funwavementioning

confidence: 99%

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Rollano

Tran

et al. 2020

JMSE

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Industry-specific tools for analyzing and optimizing the design of wave energy converters (WECs) and associated power systems are essential to advancing marine renewable energy. This study aims to quantify the influence of phase information on the device power output of a virtual WEC array. We run the phase-resolving wave model FUNWAVE-TVD (Total Variation Diminishing) to generate directional waves at the PacWave South site offshore from Newport, Oregon, where future WECs are expected to be installed for testing. The two broad cases presented correspond to mean wave climates during warm months (March–August) and cold months (September–February). FUNWAVE-TVD time series of sea-surface elevation are then used in WEC-Sim, a time domain numerical model, to simulate the hydrodynamic response of each device in the array and estimate their power output. For comparison, WEC-Sim is also run with wave energy spectra calculated from the FUNWAVE-TVD simulations, which do not retain phase information, and with wave spectra computed using the phase-averaged model Simulating WAves Nearshore (SWAN). The use of spectral data in WEC-Sim requires a conversion from frequency to time domain by means of random superposition of wave components, which are not necessarily consistent because of the linear assumption implicit in this method. Thus, power response is characterized by multiple realizations of the wave climates.

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Section: Funwavementioning

confidence: 99%

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Rollano

Tran

et al. 2020

JMSE

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show abstract

“…The directional spectrum was discretised based on an equal energy components method because, when compared with a constant step method, is more efficient for a small number of directional components (Grassa, 1990).…”

Section: àP=2mentioning

confidence: 99%

Numerical modelling of deformation of multi-directional random waves over a varying topography

Oliveira

2007

Ocean Engineering

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“…The approach followed here is inspired to the method used by Kirby et al (1992), who separated the whole wave spectrum into several narrow bands and for each of these solved the time-dependent MSE. Here we directly use the Fourier Transform of the time-dependent MSE and we solve the resulting equations in the frequency domain, thus similarly to Grassa (1990) and Panchang et al (1990). The problem therefore reduces to the solution of the traditional elliptic MSE and the time series of the surface elevation is then recovered by means of the Inverse Fourier Transform.…”

Section: Introductionmentioning

confidence: 99%

Simulation of small-amplitude frequency-dispersive transient waves by means of the mild-slope equation

Bellotti

Cecioni

Girolamo

2008

Coastal Engineering

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Directional Random Waves Propagation on Beaches

Cited by 5 publications

References 0 publications

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Numerical modelling of deformation of multi-directional random waves over a varying topography

Simulation of small-amplitude frequency-dispersive transient waves by means of the mild-slope equation

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