High-resolution hindcasts for U.S. wave energy resource characterization

被引:0
作者
Yang Z. [1 ]
Neary V.S. [2 ]
机构
[1] Marine Sciences Laboratory in the Pacific Northwest National Laboratory, 1100 Dexter Ave North, Suite 500, Seattle, WA
[2] Sandia National Laboratories, P.O. Box 5800, Albuquerque, 87185-MS1124, NM
来源
International Marine Energy Journal | 2020年 / 3卷 / 02期
关键词
Classification systems; Model validation; Resource characterization; U.S. regional wave hindcast; Unstructured-grid wave model;
D O I
10.36688/imej.3.65-71
中图分类号
学科分类号
摘要
The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed. © 2020, European Wave and Tidal Energy Conference. All rights reserved.
引用
收藏
页码:65 / 71
页数:6
相关论文
共 14 条
[1]  
Marine energy - wave, tidal and other water current converters - Part 101: Wave energy resource assessment and characterization, (2015)
[2]  
Mapping and Assessment of the United States Ocean Wave Energy Resource, (2011)
[3]  
Tolman H.L., User manual and system documentation of WAVEWATCH III® version 4.18, (2014)
[4]  
SWAN SWAN, User Manual, Cycle III version 41.01A, (2015)
[5]  
Ardhuin F., Rogers E., Babanin A.V., Filipot J.F., Magne R., Roland A., van der Westhuysen A., Queffeulou P., Lefevre J.M., Aouf L., Collard F., Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, Journal of Physical Oceanography, 40, 9, pp. 1917-1941, (2010)
[6]  
Dietrich J.C., Tanaka S., Westerink J.J., Dawson C.N., Luettich R.A., Zijlema M., Holthuijsen L.H., Smith J.M., Westerink L.G., Westerink H.J., Performance of the Unstructured-Mesh, SWAN plus ADCIRC Model in Computing Hurricane Waves and Surge, Journal of Scientific Computing, 52, 2, pp. 468-497, (2012)
[7]  
Yang Z., Wu W.C., Wang T., Castrucci L., High-Resolution Regional Wave Hindcast for the U.S. West Coast, (2018)
[8]  
Allahdadi M.N., Gunawan B., Lai J., He R.Y., Neary V.S., Development and validation of a regional-scale high-resolution unstructured model for wave energy resource characterization along the US East Coast, Renewable Energy, 136, (2019)
[9]  
Yang Z., Wu W.C., Wang T., Garcia-Medina G., Castrucci L., High-Resolution Regional Wave Hindcast for the U.S. Alaska Coast, (2019)
[10]  
Yang Z.Q., Neary V.S., Wang T.P., Gunawan B.D., Dallman A.R., Wu W.C., A wave model test bed study for wave energy resource characterization, Renewable Energy, 114, pp. 132-144, (2017)