Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images
Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged g...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Peng, Chi [verfasserIn] Tian, Shou-ceng [verfasserIn] Li, Gen-sheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of hydrodynamics - Singapore : Springer Singapore, 2006, 33(2021), 1 vom: Feb., Seite 127-139 |
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| Übergeordnetes Werk: |
volume:33 ; year:2021 ; number:1 ; month:02 ; pages:127-139 |
| Links: |
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| DOI / URN: |
10.1007/s42241-021-0016-x |
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| Katalog-ID: |
SPR043437168 |
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| 245 | 1 | 0 | |a Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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| 520 | |a Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. | ||
| 700 | 1 | |a Tian, Shou-ceng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Gen-sheng |e verfasserin |4 aut | |
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10.1007/s42241-021-0016-x doi (DE-627)SPR043437168 (DE-599)SPRs42241-021-0016-x-e (SPR)s42241-021-0016-x-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE Peng, Chi verfasserin aut Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. Tian, Shou-ceng verfasserin aut Li, Gen-sheng verfasserin aut Enthalten in Journal of hydrodynamics Singapore : Springer Singapore, 2006 33(2021), 1 vom: Feb., Seite 127-139 (DE-627)557879760 (DE-600)2406316-2 1878-0342 nnns volume:33 year:2021 number:1 month:02 pages:127-139 https://dx.doi.org/10.1007/s42241-021-0016-x 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_121 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 33 2021 1 02 127-139 |
| spelling |
10.1007/s42241-021-0016-x doi (DE-627)SPR043437168 (DE-599)SPRs42241-021-0016-x-e (SPR)s42241-021-0016-x-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE Peng, Chi verfasserin aut Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. Tian, Shou-ceng verfasserin aut Li, Gen-sheng verfasserin aut Enthalten in Journal of hydrodynamics Singapore : Springer Singapore, 2006 33(2021), 1 vom: Feb., Seite 127-139 (DE-627)557879760 (DE-600)2406316-2 1878-0342 nnns volume:33 year:2021 number:1 month:02 pages:127-139 https://dx.doi.org/10.1007/s42241-021-0016-x 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_121 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 33 2021 1 02 127-139 |
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10.1007/s42241-021-0016-x doi (DE-627)SPR043437168 (DE-599)SPRs42241-021-0016-x-e (SPR)s42241-021-0016-x-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE Peng, Chi verfasserin aut Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. Tian, Shou-ceng verfasserin aut Li, Gen-sheng verfasserin aut Enthalten in Journal of hydrodynamics Singapore : Springer Singapore, 2006 33(2021), 1 vom: Feb., Seite 127-139 (DE-627)557879760 (DE-600)2406316-2 1878-0342 nnns volume:33 year:2021 number:1 month:02 pages:127-139 https://dx.doi.org/10.1007/s42241-021-0016-x 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_121 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 33 2021 1 02 127-139 |
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10.1007/s42241-021-0016-x doi (DE-627)SPR043437168 (DE-599)SPRs42241-021-0016-x-e (SPR)s42241-021-0016-x-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE Peng, Chi verfasserin aut Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. Tian, Shou-ceng verfasserin aut Li, Gen-sheng verfasserin aut Enthalten in Journal of hydrodynamics Singapore : Springer Singapore, 2006 33(2021), 1 vom: Feb., Seite 127-139 (DE-627)557879760 (DE-600)2406316-2 1878-0342 nnns volume:33 year:2021 number:1 month:02 pages:127-139 https://dx.doi.org/10.1007/s42241-021-0016-x 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_121 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 33 2021 1 02 127-139 |
| allfieldsSound |
10.1007/s42241-021-0016-x doi (DE-627)SPR043437168 (DE-599)SPRs42241-021-0016-x-e (SPR)s42241-021-0016-x-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE Peng, Chi verfasserin aut Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. Tian, Shou-ceng verfasserin aut Li, Gen-sheng verfasserin aut Enthalten in Journal of hydrodynamics Singapore : Springer Singapore, 2006 33(2021), 1 vom: Feb., Seite 127-139 (DE-627)557879760 (DE-600)2406316-2 1878-0342 nnns volume:33 year:2021 number:1 month:02 pages:127-139 https://dx.doi.org/10.1007/s42241-021-0016-x 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_121 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 33 2021 1 02 127-139 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR043437168</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112042718.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210309s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42241-021-0016-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR043437168</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs42241-021-0016-x-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42241-021-0016-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Peng, Chi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. 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Peng, Chi |
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Peng, Chi ddc 550 Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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550 ASE Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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Peng, Chi |
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Journal of hydrodynamics |
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Peng, Chi Tian, Shou-ceng Li, Gen-sheng |
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determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
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Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
| abstract |
Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. |
| abstractGer |
Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. |
| abstract_unstemmed |
Abstract To accurately determine the shedding frequency of the cavitation cloud in a submerged cavitation jet, the spectral analysis and the proper orthogonal decomposition (POD) for high-speed photography images are performed. The spectrums of 6 different kinds of image signals (the area-averaged gray level, the line-averaged gray level, the point gray level, the cavitation length, width, and area) are calculated and compared. The line-averaged gray level is found to be optimal in determining the shedding frequency but an accurate frequency can only be obtained in the stable-frequency zone where the cavitation cloud sheds. In repeated experiments, the plateau-shape distribution of the main frequency is established with a deviation of 10.8%. A revised Reynolds number Re′ is defined and the shedding frequency can be correlated to Re′ by a power law when the cavitation number is less than 0.02. This relationship is validated by the experimental data in literature. The first mode of the POD characterizes the ensemble-average of the cavitation cloud while the second mode is the major part of the cavitation cloud transient components. The modes 2–5 are organized in pairs, which confirms the periodic feature of the cavitation cloud in the submerged cavitation jet. Near the nozzle exit, the modes 2–5 are symmetrically distributed in the jet shear layer. The shedding frequency of the cloud cavitation can also be precisely determined by performing the spectral analysis of the weighting coefficients of the mode 2. This paper shows that the two parameters, namely, the line-averaged gray level and the weighting coefficients of the mode 2, can be confidently used to calculate the shedding frequency of the cavitation cloud in a submerged cavitation jet. |
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| container_issue |
1 |
| title_short |
Determination of the shedding frequency of cavitation cloud in a submerged cavitation jet based on high-speed photography images |
| url |
https://dx.doi.org/10.1007/s42241-021-0016-x |
| remote_bool |
true |
| author2 |
Tian, Shou-ceng Li, Gen-sheng |
| author2Str |
Tian, Shou-ceng Li, Gen-sheng |
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c |
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| doi_str |
10.1007/s42241-021-0016-x |
| up_date |
2024-07-03T18:39:46.314Z |
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1803584260085383169 |
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| score |
7.4021015 |