Design optimization of wind turbine blades for reduction of airfoil self-noise
Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the r...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Seunghoon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 27(2013), 2 vom: Feb., Seite 413-420 |
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| Übergeordnetes Werk: |
volume:27 ; year:2013 ; number:2 ; month:02 ; pages:413-420 |
| Links: |
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| DOI / URN: |
10.1007/s12206-012-1254-1 |
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SPR025306332 |
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| 245 | 1 | 0 | |a Design optimization of wind turbine blades for reduction of airfoil self-noise |
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| 520 | |a Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. | ||
| 650 | 4 | |a Airfoil self-noise |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Blade design |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Design optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Noise reduction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wind turbine |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Lee, Soogab |4 aut | |
| 700 | 1 | |a Ryi, Jaeha |4 aut | |
| 700 | 1 | |a Choi, Jong-Soo |4 aut | |
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10.1007/s12206-012-1254-1 doi (DE-627)SPR025306332 (SPR)s12206-012-1254-1-e DE-627 ger DE-627 rakwb eng Lee, Seunghoon verfasserin aut Design optimization of wind turbine blades for reduction of airfoil self-noise 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 Lee, Soogab aut Ryi, Jaeha aut Choi, Jong-Soo aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 27(2013), 2 vom: Feb., Seite 413-420 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:27 year:2013 number:2 month:02 pages:413-420 https://dx.doi.org/10.1007/s12206-012-1254-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2013 2 02 413-420 |
| spelling |
10.1007/s12206-012-1254-1 doi (DE-627)SPR025306332 (SPR)s12206-012-1254-1-e DE-627 ger DE-627 rakwb eng Lee, Seunghoon verfasserin aut Design optimization of wind turbine blades for reduction of airfoil self-noise 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 Lee, Soogab aut Ryi, Jaeha aut Choi, Jong-Soo aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 27(2013), 2 vom: Feb., Seite 413-420 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:27 year:2013 number:2 month:02 pages:413-420 https://dx.doi.org/10.1007/s12206-012-1254-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2013 2 02 413-420 |
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10.1007/s12206-012-1254-1 doi (DE-627)SPR025306332 (SPR)s12206-012-1254-1-e DE-627 ger DE-627 rakwb eng Lee, Seunghoon verfasserin aut Design optimization of wind turbine blades for reduction of airfoil self-noise 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 Lee, Soogab aut Ryi, Jaeha aut Choi, Jong-Soo aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 27(2013), 2 vom: Feb., Seite 413-420 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:27 year:2013 number:2 month:02 pages:413-420 https://dx.doi.org/10.1007/s12206-012-1254-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2013 2 02 413-420 |
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10.1007/s12206-012-1254-1 doi (DE-627)SPR025306332 (SPR)s12206-012-1254-1-e DE-627 ger DE-627 rakwb eng Lee, Seunghoon verfasserin aut Design optimization of wind turbine blades for reduction of airfoil self-noise 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 Lee, Soogab aut Ryi, Jaeha aut Choi, Jong-Soo aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 27(2013), 2 vom: Feb., Seite 413-420 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:27 year:2013 number:2 month:02 pages:413-420 https://dx.doi.org/10.1007/s12206-012-1254-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2013 2 02 413-420 |
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10.1007/s12206-012-1254-1 doi (DE-627)SPR025306332 (SPR)s12206-012-1254-1-e DE-627 ger DE-627 rakwb eng Lee, Seunghoon verfasserin aut Design optimization of wind turbine blades for reduction of airfoil self-noise 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 Lee, Soogab aut Ryi, Jaeha aut Choi, Jong-Soo aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 27(2013), 2 vom: Feb., Seite 413-420 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:27 year:2013 number:2 month:02 pages:413-420 https://dx.doi.org/10.1007/s12206-012-1254-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2013 2 02 413-420 |
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Enthalten in Journal of mechanical science and technology 27(2013), 2 vom: Feb., Seite 413-420 volume:27 year:2013 number:2 month:02 pages:413-420 |
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Lee, Seunghoon @@aut@@ Lee, Soogab @@aut@@ Ryi, Jaeha @@aut@@ Choi, Jong-Soo @@aut@@ |
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|
| author |
Lee, Seunghoon |
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Lee, Seunghoon misc Airfoil self-noise misc Blade design misc Design optimization misc Noise reduction misc Wind turbine Design optimization of wind turbine blades for reduction of airfoil self-noise |
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Design optimization of wind turbine blades for reduction of airfoil self-noise Airfoil self-noise (dpeaa)DE-He213 Blade design (dpeaa)DE-He213 Design optimization (dpeaa)DE-He213 Noise reduction (dpeaa)DE-He213 Wind turbine (dpeaa)DE-He213 |
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misc Airfoil self-noise misc Blade design misc Design optimization misc Noise reduction misc Wind turbine |
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misc Airfoil self-noise misc Blade design misc Design optimization misc Noise reduction misc Wind turbine |
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Design optimization of wind turbine blades for reduction of airfoil self-noise |
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Lee, Seunghoon |
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Lee, Seunghoon Lee, Soogab Ryi, Jaeha Choi, Jong-Soo |
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Elektronische Aufsätze |
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Lee, Seunghoon |
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10.1007/s12206-012-1254-1 |
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design optimization of wind turbine blades for reduction of airfoil self-noise |
| title_auth |
Design optimization of wind turbine blades for reduction of airfoil self-noise |
| abstract |
Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
| abstractGer |
Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
| abstract_unstemmed |
Abstract To reduce airfoil self-noise from a 10 kW wind turbine, we modified the airfoil shape and planform of a wind turbine blade. To obtain the optimal blade design, we used optimization techniques based on genetic algorithms. The optimized airfoil was first determined based on a section of the rotor blade, and then the optimized blade was designed with this airfoil. The airfoil self-noise from the rotor blades was predicted by using a semi-empirical model. The numerical analysis indicates that the level of the airfoil self-noise from the optimized blade is 2.3 dB lower than that from the baseline blade at the rated wind speed. A wind tunnel experiment was also performed to validate the design optimization. The baseline and optimized rotors were scaled down by a factor of 5.71 for the wind tunnel test. The experimental results showed that airfoil self-noise is reduced by up to 2.6 dB. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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Design optimization of wind turbine blades for reduction of airfoil self-noise |
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https://dx.doi.org/10.1007/s12206-012-1254-1 |
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Lee, Soogab Ryi, Jaeha Choi, Jong-Soo |
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