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Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models

Author

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  • Mizuki Konagaya

    (Rera Tech Inc., 5-1-1 Fukae-minami, Higashinada-ku, Kobe 658-0022, Japan
    Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe 658-0022, Japan)

  • Teruo Ohsawa

    (Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe 658-0022, Japan)

  • Toshinari Mito

    (Rera Tech Inc., 5-1-1 Fukae-minami, Higashinada-ku, Kobe 658-0022, Japan)

  • Takeshi Misaki

    (Rera Tech Inc., 5-1-1 Fukae-minami, Higashinada-ku, Kobe 658-0022, Japan
    Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe 658-0022, Japan)

  • Taro Maruo

    (Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe 658-0022, Japan)

  • Yasuyuki Baba

    (Shirahama Oceanographic Observatory, 2500-106 Katata, Shirahama, Nishimuro, Wakayama 649-2201, Japan)

Abstract

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous.

Suggested Citation

  • Mizuki Konagaya & Teruo Ohsawa & Toshinari Mito & Takeshi Misaki & Taro Maruo & Yasuyuki Baba, 2022. "Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models," Resources, MDPI, vol. 11(11), pages 1-18, October.
  • Handle: RePEc:gam:jresou:v:11:y:2022:i:11:p:100-:d:958130
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    References listed on IDEAS

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    1. Nicolas Gasset & Mathieu Landry & Yves Gagnon, 2012. "A Comparison of Wind Flow Models for Wind Resource Assessment in Wind Energy Applications," Energies, MDPI, vol. 5(11), pages 1-35, October.
    2. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    3. Mattar, Cristian & Borvarán, Dager, 2016. "Offshore wind power simulation by using WRF in the central coast of Chile," Renewable Energy, Elsevier, vol. 94(C), pages 22-31.
    4. Takeshi Misaki & Teruo Ohsawa & Mizuki Konagaya & Susumu Shimada & Yuko Takeyama & Satoshi Nakamura, 2019. "Accuracy Comparison of Coastal Wind Speeds between WRF Simulations Using Different Input Datasets in Japan," Energies, MDPI, vol. 12(14), pages 1-20, July.
    5. Lattawan Niyomtham & Charoenporn Lertsathittanakorn & Jompob Waewsak & Yves Gagnon, 2022. "Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand," Energies, MDPI, vol. 15(9), pages 1-19, April.
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