Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications
Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must...
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Gespeichert in:
| Autor*in: |
Li, Y. G. [verfasserIn] Pilidis, P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Frontiers of energy and power engineering in China - Beijing : Higher Education Press, 2007, 3(2009), 4 vom: 21. Mai |
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| Übergeordnetes Werk: |
volume:3 ; year:2009 ; number:4 ; day:21 ; month:05 |
| Links: |
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| DOI / URN: |
10.1007/s11708-009-0042-9 |
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| Katalog-ID: |
SPR021969086 |
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| 520 | |a Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. | ||
| 650 | 4 | |a gas turbine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a engine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a performance adaptation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a performance matching |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a design-point performance simulation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a influence coefficient matrix |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a genetic algorithm |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pilidis, P. |e verfasserin |4 aut | |
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10.1007/s11708-009-0042-9 doi (DE-627)SPR021969086 (SPR)s11708-009-0042-9-e DE-627 ger DE-627 rakwb eng 690 ASE Li, Y. G. verfasserin aut Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Pilidis, P. verfasserin aut Enthalten in Frontiers of energy and power engineering in China Beijing : Higher Education Press, 2007 3(2009), 4 vom: 21. Mai (DE-627)546007775 (DE-600)2389481-7 1673-7504 nnns volume:3 year:2009 number:4 day:21 month:05 https://dx.doi.org/10.1007/s11708-009-0042-9 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_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_266 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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 3 2009 4 21 05 |
| spelling |
10.1007/s11708-009-0042-9 doi (DE-627)SPR021969086 (SPR)s11708-009-0042-9-e DE-627 ger DE-627 rakwb eng 690 ASE Li, Y. G. verfasserin aut Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Pilidis, P. verfasserin aut Enthalten in Frontiers of energy and power engineering in China Beijing : Higher Education Press, 2007 3(2009), 4 vom: 21. Mai (DE-627)546007775 (DE-600)2389481-7 1673-7504 nnns volume:3 year:2009 number:4 day:21 month:05 https://dx.doi.org/10.1007/s11708-009-0042-9 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_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_266 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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 3 2009 4 21 05 |
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10.1007/s11708-009-0042-9 doi (DE-627)SPR021969086 (SPR)s11708-009-0042-9-e DE-627 ger DE-627 rakwb eng 690 ASE Li, Y. G. verfasserin aut Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Pilidis, P. verfasserin aut Enthalten in Frontiers of energy and power engineering in China Beijing : Higher Education Press, 2007 3(2009), 4 vom: 21. Mai (DE-627)546007775 (DE-600)2389481-7 1673-7504 nnns volume:3 year:2009 number:4 day:21 month:05 https://dx.doi.org/10.1007/s11708-009-0042-9 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_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_266 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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 3 2009 4 21 05 |
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10.1007/s11708-009-0042-9 doi (DE-627)SPR021969086 (SPR)s11708-009-0042-9-e DE-627 ger DE-627 rakwb eng 690 ASE Li, Y. G. verfasserin aut Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Pilidis, P. verfasserin aut Enthalten in Frontiers of energy and power engineering in China Beijing : Higher Education Press, 2007 3(2009), 4 vom: 21. Mai (DE-627)546007775 (DE-600)2389481-7 1673-7504 nnns volume:3 year:2009 number:4 day:21 month:05 https://dx.doi.org/10.1007/s11708-009-0042-9 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_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_266 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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 3 2009 4 21 05 |
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10.1007/s11708-009-0042-9 doi (DE-627)SPR021969086 (SPR)s11708-009-0042-9-e DE-627 ger DE-627 rakwb eng 690 ASE Li, Y. G. verfasserin aut Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Pilidis, P. verfasserin aut Enthalten in Frontiers of energy and power engineering in China Beijing : Higher Education Press, 2007 3(2009), 4 vom: 21. Mai (DE-627)546007775 (DE-600)2389481-7 1673-7504 nnns volume:3 year:2009 number:4 day:21 month:05 https://dx.doi.org/10.1007/s11708-009-0042-9 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_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_266 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_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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 3 2009 4 21 05 |
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Enthalten in Frontiers of energy and power engineering in China 3(2009), 4 vom: 21. Mai volume:3 year:2009 number:4 day:21 month:05 |
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G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2009</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 Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. 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| author |
Li, Y. G. |
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Li, Y. G. ddc 690 misc gas turbine misc engine misc performance adaptation misc performance matching misc design-point performance simulation misc influence coefficient matrix misc genetic algorithm Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications |
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690 ASE Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications gas turbine (dpeaa)DE-He213 engine (dpeaa)DE-He213 performance adaptation (dpeaa)DE-He213 performance matching (dpeaa)DE-He213 design-point performance simulation (dpeaa)DE-He213 influence coefficient matrix (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 |
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ddc 690 misc gas turbine misc engine misc performance adaptation misc performance matching misc design-point performance simulation misc influence coefficient matrix misc genetic algorithm |
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ddc 690 misc gas turbine misc engine misc performance adaptation misc performance matching misc design-point performance simulation misc influence coefficient matrix misc genetic algorithm |
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Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications |
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Li, Y. G. |
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Frontiers of energy and power engineering in China |
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10.1007/s11708-009-0042-9 |
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690 |
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verfasserin |
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nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications |
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Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications |
| abstract |
Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. |
| abstractGer |
Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. |
| abstract_unstemmed |
Abstract Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications. |
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Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications |
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https://dx.doi.org/10.1007/s11708-009-0042-9 |
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Pilidis, P. |
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| doi_str |
10.1007/s11708-009-0042-9 |
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2024-07-04T01:13:38.829Z |
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| score |
7.400361 |