Kriging-PSO-based shape optimization for railway wheel profile
Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railw...
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| Autor*in: |
Liu, Long [verfasserIn] Yi, Bing [verfasserIn] Shi, Xiaofei [verfasserIn] Peng, Xiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of mechanical science and technology - Korean Society of Mechanical Engineers, 2005, 38(2024), 9 vom: Sept., Seite 4921-4932 |
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| Übergeordnetes Werk: |
volume:38 ; year:2024 ; number:9 ; month:09 ; pages:4921-4932 |
| Links: |
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| DOI / URN: |
10.1007/s12206-024-0827-0 |
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| Katalog-ID: |
SPR057259461 |
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| 520 | |a Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. | ||
| 650 | 4 | |a Shape optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wheel wear |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dynamics performance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wheel-rail interaction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Geometric profile |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Shi, Xiaofei |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Xiang |e verfasserin |4 aut | |
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10.1007/s12206-024-0827-0 doi (DE-627)SPR057259461 (SPR)s12206-024-0827-0-e DE-627 ger DE-627 rakwb eng 620 VZ Liu, Long verfasserin aut Kriging-PSO-based shape optimization for railway wheel profile 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 Yi, Bing verfasserin aut Shi, Xiaofei verfasserin aut Peng, Xiang verfasserin aut Enthalten in Journal of mechanical science and technology Korean Society of Mechanical Engineers, 2005 38(2024), 9 vom: Sept., Seite 4921-4932 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:38 year:2024 number:9 month:09 pages:4921-4932 https://dx.doi.org/10.1007/s12206-024-0827-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2024 9 09 4921-4932 |
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10.1007/s12206-024-0827-0 doi (DE-627)SPR057259461 (SPR)s12206-024-0827-0-e DE-627 ger DE-627 rakwb eng 620 VZ Liu, Long verfasserin aut Kriging-PSO-based shape optimization for railway wheel profile 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 Yi, Bing verfasserin aut Shi, Xiaofei verfasserin aut Peng, Xiang verfasserin aut Enthalten in Journal of mechanical science and technology Korean Society of Mechanical Engineers, 2005 38(2024), 9 vom: Sept., Seite 4921-4932 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:38 year:2024 number:9 month:09 pages:4921-4932 https://dx.doi.org/10.1007/s12206-024-0827-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2024 9 09 4921-4932 |
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10.1007/s12206-024-0827-0 doi (DE-627)SPR057259461 (SPR)s12206-024-0827-0-e DE-627 ger DE-627 rakwb eng 620 VZ Liu, Long verfasserin aut Kriging-PSO-based shape optimization for railway wheel profile 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 Yi, Bing verfasserin aut Shi, Xiaofei verfasserin aut Peng, Xiang verfasserin aut Enthalten in Journal of mechanical science and technology Korean Society of Mechanical Engineers, 2005 38(2024), 9 vom: Sept., Seite 4921-4932 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:38 year:2024 number:9 month:09 pages:4921-4932 https://dx.doi.org/10.1007/s12206-024-0827-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2024 9 09 4921-4932 |
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10.1007/s12206-024-0827-0 doi (DE-627)SPR057259461 (SPR)s12206-024-0827-0-e DE-627 ger DE-627 rakwb eng 620 VZ Liu, Long verfasserin aut Kriging-PSO-based shape optimization for railway wheel profile 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 Yi, Bing verfasserin aut Shi, Xiaofei verfasserin aut Peng, Xiang verfasserin aut Enthalten in Journal of mechanical science and technology Korean Society of Mechanical Engineers, 2005 38(2024), 9 vom: Sept., Seite 4921-4932 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:38 year:2024 number:9 month:09 pages:4921-4932 https://dx.doi.org/10.1007/s12206-024-0827-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2024 9 09 4921-4932 |
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10.1007/s12206-024-0827-0 doi (DE-627)SPR057259461 (SPR)s12206-024-0827-0-e DE-627 ger DE-627 rakwb eng 620 VZ Liu, Long verfasserin aut Kriging-PSO-based shape optimization for railway wheel profile 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 Yi, Bing verfasserin aut Shi, Xiaofei verfasserin aut Peng, Xiang verfasserin aut Enthalten in Journal of mechanical science and technology Korean Society of Mechanical Engineers, 2005 38(2024), 9 vom: Sept., Seite 4921-4932 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:38 year:2024 number:9 month:09 pages:4921-4932 https://dx.doi.org/10.1007/s12206-024-0827-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2024 9 09 4921-4932 |
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Liu, Long |
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Liu, Long ddc 620 misc Shape optimization misc Wheel wear misc Dynamics performance misc Wheel-rail interaction misc Geometric profile Kriging-PSO-based shape optimization for railway wheel profile |
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620 VZ Kriging-PSO-based shape optimization for railway wheel profile Shape optimization (dpeaa)DE-He213 Wheel wear (dpeaa)DE-He213 Dynamics performance (dpeaa)DE-He213 Wheel-rail interaction (dpeaa)DE-He213 Geometric profile (dpeaa)DE-He213 |
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ddc 620 misc Shape optimization misc Wheel wear misc Dynamics performance misc Wheel-rail interaction misc Geometric profile |
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ddc 620 misc Shape optimization misc Wheel wear misc Dynamics performance misc Wheel-rail interaction misc Geometric profile |
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kriging-pso-based shape optimization for railway wheel profile |
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Kriging-PSO-based shape optimization for railway wheel profile |
| abstract |
Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 |
| abstractGer |
Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 |
| abstract_unstemmed |
Abstract The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2024 |
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Kriging-PSO-based shape optimization for railway wheel profile |
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https://dx.doi.org/10.1007/s12206-024-0827-0 |
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Yi, Bing Shi, Xiaofei Peng, Xiang |
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| doi_str |
10.1007/s12206-024-0827-0 |
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2024-09-10T04:50:01.085Z |
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| score |
7.3980665 |