Optimized Numerical Model Based Assessment of Wave Power Potential of Marmara Sea
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Marmara Sea, located between Black Sea and Aegean Sea, is an important sea for ocean engineering activities. In this study, wave power potential of Marmara Sea was investigated using the third generation spectral wind-wave model MIKE 21 SW with unstructured mesh. Wind data was obtained from ECMWF ERA-Interim re-analyses wind dataset at 10 m with a spatial resolution of 0.1° for the period of 1994 to 2014. The numerical model was calibrated with measured wave data from a buoy station located in Marmara Sea. Mesh optimization was also performed to obtain the most suitable mesh structure for the study area. This study is the first that dealt with the determination of wave energy potential of Marmara Sea. The numerical model results are presented in terms of monthly, seasonal and annual average of wave power flux (kW m−1). The maximum wave power flux is 1.13 kW m−1 and occurs in November. The overall annual mean wave power flux during 1994–2014 is found to be 0.27 kW m−1 in the offshore regions.
Key wordsMarmara Sea MIKE 21 SW wave power potential ECMWF buoy wave data
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This research was funded by TÜBITAK (The Scientific and Technological Research Council of Turkey) (No. 112M 413). We thank the European Centre for Medium-Range Weather Forecasts for providing the wind data, the Marine Geoscience Data System for providing the bathymetry data, and Turkish Petroleum for providing the buoy wave data.
- Abdollahzadehmoradi, Y., Özger, M., and Altunkaynak, A., 2018. Long–term macro–scale assessment of wave power of Black Sea by an optimized numerical model. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 42 (4): 1–24. DOI: 10.1007/s40996–018–0108–1.CrossRefGoogle Scholar
- Cornett, A. M., 2008. A global wave energy resource assessment. In: The Proceedings of the Eighteenth (2008) International Offshore and Polar Engineering Conference. Vancouver, 318–326.Google Scholar
- DHI, 2012. MIKE 21 spectral wave module. Scientific documentation, DHI Water & Environment.Google Scholar
- Jadidoleslam, N., Özger, M., and Ağıralioğlu, N., (2016). Wave power potential assessment of Aegean Sea with an integrated 15–year data. Renewable Energy, 86: 1045–1059. DOI: 10. 1016/j.renene.2015.09.022.Google Scholar
- Jose, F., and Stone, G. W., 2006. Forecast of nearshore wave heights using MIKE–21 spectral wave model. Gulf Coast Association of Geological Societies Transactions, 56: 323–327.Google Scholar
- Kick, C., 2011. How is 100% renewable energy possible for Turkey by 2020? Global Energy Network Institute (GENI), http://www.geni.org/.Google Scholar
- Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and Janssen, P. A. E. M., 1996. Dynamics and Modelling of Ocean Waves. Cambridge University Press, Cambridge, 560pp.Google Scholar
- Mørk, G., Barstow, S., Kabuth, A., and Pontes, M. T., 2010. Assessing the global wave energy potential. In: Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering. Shanghai, 6–11.Google Scholar
- Ramanarayanan, T. S., Williams, J. R., Dugas, W. A., Hauck, L. M., and McFarland, A. M. S., 1997. Using APEX to identify alternative practices for animal waste management: Part II. Model application. ASAE Paper 97–2209.Google Scholar
- Young, I. R., 1999. Wind Generated Ocean Waves. Ocean Engineering Book Series, Vol. 2. Elsevier, Oxford, 92pp.Google Scholar