Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization

Wu, Wei‐Cheng; Wang, Taiping; Yang, Zhaoqing; García‐Medina, Gabriel

doi:10.1016/j.renene.2020.01.077

Cited by 42 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wave spectrum was discretized with 24 directions and 29 frequencies from 0.035 Hz to 0.505 Hz. Results from Wu et al [24] show good agreement with observations and replicate the wave seasonality very well. Figure 2 shows the wave climate at the PWS site obtained by averaging the spectra over the 32 years for the warm months (March-August) and cold months (September-February) separately.…”

Section: Study Sitesupporting

confidence: 54%

“…The wave climate in Oregon is characterized by two very distinct seasons: calm summers and energetic winters (e.g., [22,23]). The offshore boundary conditions were obtained from a regional 32-year wave climate hindcast study [24]. Wave spectra were collected every hour near the study site between January 1, 1979 and December 31, 2010.…”

Section: Study Sitementioning

confidence: 99%

“…For a phase-averaged wave simulation at the PWS site we implement the third-generation spectral model SWAN [25]. This open-source model has been widely used in wave resource assessments because of its accuracy and computational efficiency (e.g., [22,24,[26][27][28][29]). It is likely that when estimating the wave climate at a study location a third-generation wave model will be implemented given that these types of models meet international standards for conducting wave resource assessments [30].…”

Section: Swanmentioning

confidence: 99%

See 2 more Smart Citations

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

Rollano

Tran

et al. 2020

JMSE

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Study Sitesupporting

confidence: 54%

Section: Study Sitementioning

confidence: 99%

Section: Swanmentioning

confidence: 99%

See 1 more Smart Citation

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

Rollano

Tran

et al. 2020

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this report, the theoretical resource results and data are from a forthcoming manuscript authored by researchers at NREL and PNNL (Kilcher, Garcia-Medina, and Yang 2021). This data set is distinct from the new high-resolution wave hindcasts because the methodology required a more detailed breakdown of the wave field (i.e., directional spectra and wave energy source terms) than is contained in the new high-resolution wave data set generated by PNNL and Sandia (Yang and Neary 2020;Wu et al 2020;Allahdadi et al 2019). The forthcoming manuscript builds on the previous DOE-funded resource assessment and resolves several limitations of that work that were identified in an NRC review (Jacobson, Hagerman, and Scott 2011;National Research Council 2013).…”

Section: Ocean Wave Energymentioning

confidence: 99%

Marine Energy in the United States: An Overview of Opportunities

Kilcher

Fogarty

Lawson

2021

View full text Add to dashboard Cite

show abstract

“…In the framework of wave resource assessment studies, data validation, variability and uncertainty measures, and analysis of extreme waves should also be included. See, e.g., [4,[19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Joint Modelling of Wave Energy Flux and Wave Direction

Soukissian

Karathanasi

2021

Processes

View full text Add to dashboard Cite

In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

show abstract

Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization

Cited by 42 publications

References 33 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

Marine Energy in the United States: An Overview of Opportunities

Joint Modelling of Wave Energy Flux and Wave Direction

Contact Info

Product

Resources

About