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Yıl 2022, Cilt: 6 Sayı: 4, 285 - 297, 20.12.2022

Alper Yıldırım

https://doi.org/10.26701/ems.1195271

Öz

Kaynakça

  • [1] Yıldızhan, Ş., Çalık, A., Özcanlı, M., Serin, H., (2018). Bio-composite materials: a short review of recent trends, mechanical and chemical properties, and applications. European Mechanical Science. 2(3): 83–91. doi: 10.26701/ems.369005.
  • [2] Mbungu, N.T., Naidoo, R.M., Bansal, R.C., Siti, M.W., Tungadio, D.H., (2020). An overview of renewable energy resources and grid integration for commercial building applications. Journal of Energy Storage. 29(December 2019): 101385. doi: 10.1016/j.est.2020.101385.
  • [3] Moriarty, P., Honnery, D., (2018). Global renewable energy resources and use in 2050. Managing Global Warming: An Interface of Technology and Human Issues. (November): 221–35. doi: 10.1016/B978-0-12-814104-5.00006-5.
  • [4] Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N., Gorini, R., (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews. 24(January): 38–50. doi: 10.1016/j.esr.2019.01.006.
  • [5] Minesto., (2020). Ocean energy. https://minesto.com/about-us. [accessed December 12, 2020].
  • [6] Kabir, A., Lemongo-Tchamba, I., Fernandez, A., (2015). An assessment of available ocean current hydrokinetic energy near the North Carolina shore. Renewable Energy. 80: 301–7. doi: 10.1016/j.renene.2015.02.011.
  • [7] Bento, P.M.R., Pombo, J.A.N., Mendes, R.P.G., Calado, M.R.A., Mariano, S.J.P.S., (2021). Ocean wave energy forecasting using optimised deep learning neural networks. Ocean Engineering. 219(December 2019): 108372. doi: 10.1016/j.oceaneng.2020.108372.
  • [8] Cenedese, C., n.d. Ocean current. https://www.britannica.com/science/ocean-current#ref301646. [accessed February 26, 2021].
  • [9] Hays, G.C., (2017). Ocean currents and marine life. Current Biology. 27(11): R470–3. doi: 10.1016/j.cub.2017.01.044.
  • [10] Neelamani, S., Al-Osairi, Y., (2019). Probability distribution, statistical characteristics, and power potential of seawater velocity around boubyan island in Kuwait. Journal of Engineering Research (Kuwait). 7(2): 143–66.
  • [11] Canepa, E., Pensieri, S., Bozzano, R., Faimali, M., Traverso, P., Cavaleri, L., (2015). The ODAS Italia 1 buoy: More than forty years of activity in the Ligurian Sea. Progress in Oceanography. 135(January 2019): 48–63. doi: 10.1016/j.pocean.2015.04.005.
  • [12] Chiri, H., Abascal, A.J., Castanedo, S., Antolínez, J.A.A., Liu, Y., Weisberg, R.H., et al., (2019). Statistical simulation of ocean current patterns using autoregressive logistic regression models: A case study in the Gulf of Mexico. Ocean Modelling. 136(January): 1–12. doi: 10.1016/j.ocemod.2019.02.010.
  • [13] Mandal, S., Sil, S., Gangopadhyay, A., Murty, T., Swain, D., (2018). On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal. Journal of Operational Oceanography. 11(2): 65–81. doi: 10.1080/1755876X.2018.1479571.
  • [14] Shay, L.K., C.Graber, H., B.Ross, D., Rickey, D.C., (1995). Mesoscale Ocean Surface Current Structure Detected by High-Frequency Radar. Journal of Atmospheric and Oceanic Technology. 12(4): 881–900. doi: https://doi.org/10.1175/1520-0426(1995)012<0881:MOSCSD>2.0.CO;2.
  • [15] Paduan, J.D., Rosenfeld, L.K., (1996). Remotely sensed surface currents in Monterey Bay from shore-based HF radar (Coastal Ocean Dynamics Application Radar). Journal of Geophysical Research C: Oceans. 101(C9): 20669–86. doi: 10.1029/96JC01663.
  • [16] Ullman, D.S., Codiga, D.L., (2004). Seasonal variation of a coastal jet in the Long Island Sound outflow region based on HF radar and Doppler current observations. Journal of Geophysical Research C: Oceans. 109(7): 1–15. doi: 10.1029/2002JC001660.
  • [17] Ramp, S.R., Barrick, D.E., Ito, T., Cook, M.S., (2008). Variability of the Kuroshio current south Sagami Bay as observed using long-range coastal HF radars. Journal of Geophysical Research: Oceans. 113(6): 1–15. doi: 10.1029/2007JC004132. [18] Gough, M.K., Garfield, N., Shaw, E.M., (2010). An analysis of HF radar measured surface currents to determine tidal , wind ‐ forced , and seasonal circulation in the Gulf of the Farallones , California , United States 115: 1–19. doi: 10.1029/2009JC005644.
  • [19] Roarty, H., Glenn, S., Kohut, J., Gong, D., Handel, E., Rivera, E., et al., (2010). Operation and application of a regional high-frequency radar network in the Mid-Atlantic Bight. Marine Technology Society Journal. 44(6): 133–45. doi: 10.4031/MTSJ.44.6.5.
  • [20] Shay, L.K., Martinez-Pedraja, J., Cook, T.M., Haus, B.K., Weisberg, R.H., (2007). High-frequency radar mapping of surface currents using WERA. Journal of Atmospheric and Oceanic Technology. 24(3): 484–503. doi: 10.1175/JTECH1985.1.
  • [21] Freilich, M.H., Dunbar, R.S., (1999). The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys. Journal of Geophysical Research: Oceans. 104(C5): 11231–46. doi: 10.1029/1998jc900091.
  • [22] Mears, C.A., Smith, D.K., Wentz, F.J., (2001). Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to 1997. Journal of Geophysical Research: Oceans. 106(C6): 11719–29. doi: 10.1029/1999jc000097.
  • [23] Le Traon, P.Y., (2013). From satellite altimetry to Argo and operational oceanography: Three revolutions in oceanography. Ocean Science. 9(5): 901–15. doi: 10.5194/os-9-901-2013.
  • [24] Wagner, V., Hageberg, A.A., Michelsen, C., (2003). EGOS-European Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas. Elsevier Oceanography Series. 69(C): 340–4. doi: 10.1016/S0422-9894(03)80054-5.
  • [25] Petersen, G.N., (2017). Meteorological buoy measurements in the Iceland Sea, 2007-2009. Earth System Science Data. 9(2): 779–89. doi: 10.5194/essd-9-779-2017.
  • [26] Oliveira, L.R., Piola, A.R., Mata, M.M., Soares, I.D., (2009). Brazil Current surface circulation and energetics observed from drifting buoys. Journal of Geophysical Research: Oceans. 114(10): 1–12. doi: 10.1029/2008JC004900.
  • [27] Albani, A., Ibrahim, M.Z., (2020). The Probability Density Distribution for Ocean Current Speed at Selected Sites THE PROBABILITY DENSITY DISTRIBUTION FOR OCEAN CURRENT SPEED AT SELECTED SITES IN (October). doi: 10.31838/jcr.07.19.607.
  • [28] Chu, P.C., (2008). Weibull distribution for the global surface current speeds obtained from satellite altimetry. International Geoscience and Remote Sensing Symposium (IGARSS). 3(1): 11–5. doi: 10.1109/IGARSS.2008.4779282.
  • [29] Chu, P.C., (2008). Probability distribution function of the upper equatorial Pacific current speeds. Geophysical Research Letters. 35(12). doi: 10.1029/2008GL033669.
  • [30] Kim, D.K., Wong, E.W.C., Lee, E.B., Yu, S.Y., Kim, Y.T., (2019). A method for the empirical formulation of current profile. Ships and Offshore Structures. 14(2): 176–92. doi: 10.1080/17445302.2018.1488340.
  • [31] Ashkenazy, Y., Gildor, H., (2011). On the probability and spatial distribution of ocean surface currents. Journal of Physical Oceanography. 41(12): 2295–306. doi: 10.1175/JPO-D-11-04.1.
  • [32] Bilgili, M., Sahin, B., (2009). Investigation of Wind Energy Density in the Southern and Southwestern Region of Turkey. Journal of Energy Engineering. 135(1): 12–20. doi: 10.1061/(asce)0733-9402(2009)135:1(12).
  • [33] Kaplan, Y.A., (2017). Determination of Weibull parameters by different numerical methods and analysis of wind power density in Osmaniye, Turkey. Scientia Iranica. 24(6): 3204–12. doi: 10.24200/sci.2017.4354.
  • [34] Akpinar, E.K., Akpinar, S., (2004). An Analysis of the Wind Energy Potential of Elazig, Turkey. International Journal of Green Energy. 1(2): 193–207. doi: 10.1081/ge-120038752.
  • [35] Kaplan, Y.A., (2020). Determination of Weibull parameters using the standard deviation method and performance comparison at different locations. Scientia Iranica. 27(6 D): 3075–83. doi: 10.24200/SCI.2019.50323.1632.

Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey

Yıl 2022, Cilt: 6 Sayı: 4, 285 - 297, 20.12.2022

Alper Yıldırım

https://doi.org/10.26701/ems.1195271

Öz

Sea currents have the potential to supply electricity from a renewable energy source to coastal regions. The assessment of the potential energy that could be generated is the first step toward developing this resource. In this study, the data was collected at 5 m and 35 m depths below the sea surface level, including sea current velocity and direction. A detailed field measurement, of the probability of sea water velocity at three stations (Antalya, Silivri, Istanbul) for 5 months is carried out. The sea current power density values in these stations were 10.41, 4.92, and 7.91 W/m2 at 5 m depth, respectively. Besides, average sea current power density values were seen to be closely arranged with 11.44, 4.07, and 9.06 W/m2 at 35 depths, respectively. In addition, statistical analysis applying Weibull and Rayleigh models is also presented. It is shown that the use of a Weibull probability distribution facilitates the analysis of sea velocity conditions and is also able to predict the power density with a high degree of accuracy. The results of this study are useful for the understanding of marine hydrodynamics of these areas, where sea current power projects may be started in Turkey.

Anahtar Kelimeler

Marine hydrodynamics Ocean power Sea current velocity Turkish seas Weibull and Rayleigh probability function.

Kaynakça

  • [1] Yıldızhan, Ş., Çalık, A., Özcanlı, M., Serin, H., (2018). Bio-composite materials: a short review of recent trends, mechanical and chemical properties, and applications. European Mechanical Science. 2(3): 83–91. doi: 10.26701/ems.369005.
  • [2] Mbungu, N.T., Naidoo, R.M., Bansal, R.C., Siti, M.W., Tungadio, D.H., (2020). An overview of renewable energy resources and grid integration for commercial building applications. Journal of Energy Storage. 29(December 2019): 101385. doi: 10.1016/j.est.2020.101385.
  • [3] Moriarty, P., Honnery, D., (2018). Global renewable energy resources and use in 2050. Managing Global Warming: An Interface of Technology and Human Issues. (November): 221–35. doi: 10.1016/B978-0-12-814104-5.00006-5.
  • [4] Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N., Gorini, R., (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews. 24(January): 38–50. doi: 10.1016/j.esr.2019.01.006.
  • [5] Minesto., (2020). Ocean energy. https://minesto.com/about-us. [accessed December 12, 2020].
  • [6] Kabir, A., Lemongo-Tchamba, I., Fernandez, A., (2015). An assessment of available ocean current hydrokinetic energy near the North Carolina shore. Renewable Energy. 80: 301–7. doi: 10.1016/j.renene.2015.02.011.
  • [7] Bento, P.M.R., Pombo, J.A.N., Mendes, R.P.G., Calado, M.R.A., Mariano, S.J.P.S., (2021). Ocean wave energy forecasting using optimised deep learning neural networks. Ocean Engineering. 219(December 2019): 108372. doi: 10.1016/j.oceaneng.2020.108372.
  • [8] Cenedese, C., n.d. Ocean current. https://www.britannica.com/science/ocean-current#ref301646. [accessed February 26, 2021].
  • [9] Hays, G.C., (2017). Ocean currents and marine life. Current Biology. 27(11): R470–3. doi: 10.1016/j.cub.2017.01.044.
  • [10] Neelamani, S., Al-Osairi, Y., (2019). Probability distribution, statistical characteristics, and power potential of seawater velocity around boubyan island in Kuwait. Journal of Engineering Research (Kuwait). 7(2): 143–66.
  • [11] Canepa, E., Pensieri, S., Bozzano, R., Faimali, M., Traverso, P., Cavaleri, L., (2015). The ODAS Italia 1 buoy: More than forty years of activity in the Ligurian Sea. Progress in Oceanography. 135(January 2019): 48–63. doi: 10.1016/j.pocean.2015.04.005.
  • [12] Chiri, H., Abascal, A.J., Castanedo, S., Antolínez, J.A.A., Liu, Y., Weisberg, R.H., et al., (2019). Statistical simulation of ocean current patterns using autoregressive logistic regression models: A case study in the Gulf of Mexico. Ocean Modelling. 136(January): 1–12. doi: 10.1016/j.ocemod.2019.02.010.
  • [13] Mandal, S., Sil, S., Gangopadhyay, A., Murty, T., Swain, D., (2018). On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal. Journal of Operational Oceanography. 11(2): 65–81. doi: 10.1080/1755876X.2018.1479571.
  • [14] Shay, L.K., C.Graber, H., B.Ross, D., Rickey, D.C., (1995). Mesoscale Ocean Surface Current Structure Detected by High-Frequency Radar. Journal of Atmospheric and Oceanic Technology. 12(4): 881–900. doi: https://doi.org/10.1175/1520-0426(1995)012<0881:MOSCSD>2.0.CO;2.
  • [15] Paduan, J.D., Rosenfeld, L.K., (1996). Remotely sensed surface currents in Monterey Bay from shore-based HF radar (Coastal Ocean Dynamics Application Radar). Journal of Geophysical Research C: Oceans. 101(C9): 20669–86. doi: 10.1029/96JC01663.
  • [16] Ullman, D.S., Codiga, D.L., (2004). Seasonal variation of a coastal jet in the Long Island Sound outflow region based on HF radar and Doppler current observations. Journal of Geophysical Research C: Oceans. 109(7): 1–15. doi: 10.1029/2002JC001660.
  • [17] Ramp, S.R., Barrick, D.E., Ito, T., Cook, M.S., (2008). Variability of the Kuroshio current south Sagami Bay as observed using long-range coastal HF radars. Journal of Geophysical Research: Oceans. 113(6): 1–15. doi: 10.1029/2007JC004132. [18] Gough, M.K., Garfield, N., Shaw, E.M., (2010). An analysis of HF radar measured surface currents to determine tidal , wind ‐ forced , and seasonal circulation in the Gulf of the Farallones , California , United States 115: 1–19. doi: 10.1029/2009JC005644.
  • [19] Roarty, H., Glenn, S., Kohut, J., Gong, D., Handel, E., Rivera, E., et al., (2010). Operation and application of a regional high-frequency radar network in the Mid-Atlantic Bight. Marine Technology Society Journal. 44(6): 133–45. doi: 10.4031/MTSJ.44.6.5.
  • [20] Shay, L.K., Martinez-Pedraja, J., Cook, T.M., Haus, B.K., Weisberg, R.H., (2007). High-frequency radar mapping of surface currents using WERA. Journal of Atmospheric and Oceanic Technology. 24(3): 484–503. doi: 10.1175/JTECH1985.1.
  • [21] Freilich, M.H., Dunbar, R.S., (1999). The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys. Journal of Geophysical Research: Oceans. 104(C5): 11231–46. doi: 10.1029/1998jc900091.
  • [22] Mears, C.A., Smith, D.K., Wentz, F.J., (2001). Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to 1997. Journal of Geophysical Research: Oceans. 106(C6): 11719–29. doi: 10.1029/1999jc000097.
  • [23] Le Traon, P.Y., (2013). From satellite altimetry to Argo and operational oceanography: Three revolutions in oceanography. Ocean Science. 9(5): 901–15. doi: 10.5194/os-9-901-2013.
  • [24] Wagner, V., Hageberg, A.A., Michelsen, C., (2003). EGOS-European Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas. Elsevier Oceanography Series. 69(C): 340–4. doi: 10.1016/S0422-9894(03)80054-5.
  • [25] Petersen, G.N., (2017). Meteorological buoy measurements in the Iceland Sea, 2007-2009. Earth System Science Data. 9(2): 779–89. doi: 10.5194/essd-9-779-2017.
  • [26] Oliveira, L.R., Piola, A.R., Mata, M.M., Soares, I.D., (2009). Brazil Current surface circulation and energetics observed from drifting buoys. Journal of Geophysical Research: Oceans. 114(10): 1–12. doi: 10.1029/2008JC004900.
  • [27] Albani, A., Ibrahim, M.Z., (2020). The Probability Density Distribution for Ocean Current Speed at Selected Sites THE PROBABILITY DENSITY DISTRIBUTION FOR OCEAN CURRENT SPEED AT SELECTED SITES IN (October). doi: 10.31838/jcr.07.19.607.
  • [28] Chu, P.C., (2008). Weibull distribution for the global surface current speeds obtained from satellite altimetry. International Geoscience and Remote Sensing Symposium (IGARSS). 3(1): 11–5. doi: 10.1109/IGARSS.2008.4779282.
  • [29] Chu, P.C., (2008). Probability distribution function of the upper equatorial Pacific current speeds. Geophysical Research Letters. 35(12). doi: 10.1029/2008GL033669.
  • [30] Kim, D.K., Wong, E.W.C., Lee, E.B., Yu, S.Y., Kim, Y.T., (2019). A method for the empirical formulation of current profile. Ships and Offshore Structures. 14(2): 176–92. doi: 10.1080/17445302.2018.1488340.
  • [31] Ashkenazy, Y., Gildor, H., (2011). On the probability and spatial distribution of ocean surface currents. Journal of Physical Oceanography. 41(12): 2295–306. doi: 10.1175/JPO-D-11-04.1.
  • [32] Bilgili, M., Sahin, B., (2009). Investigation of Wind Energy Density in the Southern and Southwestern Region of Turkey. Journal of Energy Engineering. 135(1): 12–20. doi: 10.1061/(asce)0733-9402(2009)135:1(12).
  • [33] Kaplan, Y.A., (2017). Determination of Weibull parameters by different numerical methods and analysis of wind power density in Osmaniye, Turkey. Scientia Iranica. 24(6): 3204–12. doi: 10.24200/sci.2017.4354.
  • [34] Akpinar, E.K., Akpinar, S., (2004). An Analysis of the Wind Energy Potential of Elazig, Turkey. International Journal of Green Energy. 1(2): 193–207. doi: 10.1081/ge-120038752.
  • [35] Kaplan, Y.A., (2020). Determination of Weibull parameters using the standard deviation method and performance comparison at different locations. Scientia Iranica. 27(6 D): 3075–83. doi: 10.24200/SCI.2019.50323.1632.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği
Bölüm Research Article
Yazarlar

Alper Yıldırım 0000-0003-2626-1666

Yayımlanma Tarihi 20 Aralık 2022
Kabul Tarihi 24 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 6 Sayı: 4

Kaynak Göster

APA Yıldırım, A. (2022). Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey. European Mechanical Science, 6(4), 285-297. https://doi.org/10.26701/ems.1195271
AMA Yıldırım A. Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey. EMS. Aralık 2022;6(4):285-297. doi:10.26701/ems.1195271
Chicago Yıldırım, Alper. “Statistical Characteristics, Probability Distribution, and Power Potential of Sea Water Velocity in Turkey”. European Mechanical Science 6, sy. 4 (Aralık 2022): 285-97. https://doi.org/10.26701/ems.1195271.
EndNote Yıldırım A (01 Aralık 2022) Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey. European Mechanical Science 6 4 285–297.
IEEE A. Yıldırım, “Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey”, EMS, c. 6, sy. 4, ss. 285–297, 2022, doi: 10.26701/ems.1195271.
ISNAD Yıldırım, Alper. “Statistical Characteristics, Probability Distribution, and Power Potential of Sea Water Velocity in Turkey”. European Mechanical Science 6/4 (Aralık 2022), 285-297. https://doi.org/10.26701/ems.1195271.
JAMA Yıldırım A. Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey. EMS. 2022;6:285–297.
MLA Yıldırım, Alper. “Statistical Characteristics, Probability Distribution, and Power Potential of Sea Water Velocity in Turkey”. European Mechanical Science, c. 6, sy. 4, 2022, ss. 285-97, doi:10.26701/ems.1195271.
Vancouver Yıldırım A. Statistical characteristics, probability distribution, and power potential of sea water velocity in Turkey. EMS. 2022;6(4):285-97.
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