A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework
Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Zhao [verfasserIn] Zhai, Qiangqiang [verfasserIn] Song, Zhouzhou [verfasserIn] Zhu, Ping [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Hierarchical uncertainty analysis |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 64(2021), 4 vom: 11. Aug., Seite 2669-2686 |
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| Übergeordnetes Werk: |
volume:64 ; year:2021 ; number:4 ; day:11 ; month:08 ; pages:2669-2686 |
| Links: |
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| DOI / URN: |
10.1007/s00158-021-03021-y |
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| Katalog-ID: |
SPR045226873 |
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| 520 | |a Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. | ||
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| 650 | 4 | |a Hierarchical sensitivity analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Uncertainty-based design optimization |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Song, Zhouzhou |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Ping |e verfasserin |4 aut | |
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10.1007/s00158-021-03021-y doi (DE-627)SPR045226873 (SPR)s00158-021-03021-y-e DE-627 ger DE-627 rakwb eng 510 ASE 50.03 bkl Liu, Zhao verfasserin aut A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. Multilevel systems (dpeaa)DE-He213 Hierarchical decomposition (dpeaa)DE-He213 Hierarchical uncertainty analysis (dpeaa)DE-He213 Hierarchical sensitivity analysis (dpeaa)DE-He213 Uncertainty-based design optimization (dpeaa)DE-He213 Zhai, Qiangqiang verfasserin aut Song, Zhouzhou verfasserin aut Zhu, Ping verfasserin aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 64(2021), 4 vom: 11. Aug., Seite 2669-2686 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 https://dx.doi.org/10.1007/s00158-021-03021-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_267 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_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_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 50.03 ASE AR 64 2021 4 11 08 2669-2686 |
| spelling |
10.1007/s00158-021-03021-y doi (DE-627)SPR045226873 (SPR)s00158-021-03021-y-e DE-627 ger DE-627 rakwb eng 510 ASE 50.03 bkl Liu, Zhao verfasserin aut A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. Multilevel systems (dpeaa)DE-He213 Hierarchical decomposition (dpeaa)DE-He213 Hierarchical uncertainty analysis (dpeaa)DE-He213 Hierarchical sensitivity analysis (dpeaa)DE-He213 Uncertainty-based design optimization (dpeaa)DE-He213 Zhai, Qiangqiang verfasserin aut Song, Zhouzhou verfasserin aut Zhu, Ping verfasserin aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 64(2021), 4 vom: 11. Aug., Seite 2669-2686 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 https://dx.doi.org/10.1007/s00158-021-03021-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_267 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_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_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 50.03 ASE AR 64 2021 4 11 08 2669-2686 |
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10.1007/s00158-021-03021-y doi (DE-627)SPR045226873 (SPR)s00158-021-03021-y-e DE-627 ger DE-627 rakwb eng 510 ASE 50.03 bkl Liu, Zhao verfasserin aut A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. Multilevel systems (dpeaa)DE-He213 Hierarchical decomposition (dpeaa)DE-He213 Hierarchical uncertainty analysis (dpeaa)DE-He213 Hierarchical sensitivity analysis (dpeaa)DE-He213 Uncertainty-based design optimization (dpeaa)DE-He213 Zhai, Qiangqiang verfasserin aut Song, Zhouzhou verfasserin aut Zhu, Ping verfasserin aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 64(2021), 4 vom: 11. Aug., Seite 2669-2686 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 https://dx.doi.org/10.1007/s00158-021-03021-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_267 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_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_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 50.03 ASE AR 64 2021 4 11 08 2669-2686 |
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10.1007/s00158-021-03021-y doi (DE-627)SPR045226873 (SPR)s00158-021-03021-y-e DE-627 ger DE-627 rakwb eng 510 ASE 50.03 bkl Liu, Zhao verfasserin aut A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. Multilevel systems (dpeaa)DE-He213 Hierarchical decomposition (dpeaa)DE-He213 Hierarchical uncertainty analysis (dpeaa)DE-He213 Hierarchical sensitivity analysis (dpeaa)DE-He213 Uncertainty-based design optimization (dpeaa)DE-He213 Zhai, Qiangqiang verfasserin aut Song, Zhouzhou verfasserin aut Zhu, Ping verfasserin aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 64(2021), 4 vom: 11. Aug., Seite 2669-2686 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 https://dx.doi.org/10.1007/s00158-021-03021-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_267 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_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_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 50.03 ASE AR 64 2021 4 11 08 2669-2686 |
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10.1007/s00158-021-03021-y doi (DE-627)SPR045226873 (SPR)s00158-021-03021-y-e DE-627 ger DE-627 rakwb eng 510 ASE 50.03 bkl Liu, Zhao verfasserin aut A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. Multilevel systems (dpeaa)DE-He213 Hierarchical decomposition (dpeaa)DE-He213 Hierarchical uncertainty analysis (dpeaa)DE-He213 Hierarchical sensitivity analysis (dpeaa)DE-He213 Uncertainty-based design optimization (dpeaa)DE-He213 Zhai, Qiangqiang verfasserin aut Song, Zhouzhou verfasserin aut Zhu, Ping verfasserin aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 64(2021), 4 vom: 11. Aug., Seite 2669-2686 (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 https://dx.doi.org/10.1007/s00158-021-03021-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_267 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_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_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 50.03 ASE AR 64 2021 4 11 08 2669-2686 |
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Enthalten in Structural and multidisciplinary optimization 64(2021), 4 vom: 11. Aug., Seite 2669-2686 volume:64 year:2021 number:4 day:11 month:08 pages:2669-2686 |
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Liu, Zhao @@aut@@ Zhai, Qiangqiang @@aut@@ Song, Zhouzhou @@aut@@ Zhu, Ping @@aut@@ |
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Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multilevel systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hierarchical decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hierarchical uncertainty analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hierarchical sensitivity analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Uncertainty-based design optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhai, Qiangqiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Zhouzhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Ping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Structural and multidisciplinary optimization</subfield><subfield code="d">Berlin : Springer, 1989</subfield><subfield code="g">64(2021), 4 vom: 11. 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general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework |
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A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework |
| abstract |
Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Several subsystems with hierarchical relationship compose a multilevel system, each of which may have uncertainties in material properties and structural geometric parameters. Compared with the integrated strategy, hierarchical decomposition method is more effective in the investigation of the performance of multilevel systems. However, the process of hierarchical propagation of uncertainty may result in many challenging problems, such as multidimensional correlations and complex coupling of uncertainties. In this paper, a general integrated procedure of multilevel system design optimization by means of the integration of the hierarchical uncertainty analysis (HUA), hierarchical sensitivity analysis (HSA) and uncertainty-based design method is innovatively proposed. Firstly, a hierarchical framework combining R-vine copula with sparse polynomial chaos expansions is adopted to solve the problems of uncertainty quantification and propagation. After that, a mapping-based hierarchical sensitivity analysis (MHSA) method is employed to obtain sensitivity indexes of multilevel systems with multidimensional correlations. At last, the probabilistic target cascade analysis method is used to accomplish the multilevel design optimization considering multilevel uncertainty. The proposed procedure is then applied to solve the material–structure integrated design problem of an automotive fiber composite shock tower. Results show that the proposed procedure can achieve a weight reduction compared with the initial design under the premise of meeting the structural performance requirements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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A general integrated procedure for uncertainty-based design optimization of multilevel systems by hierarchical decomposition framework |
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https://dx.doi.org/10.1007/s00158-021-03021-y |
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Zhai, Qiangqiang Song, Zhouzhou Zhu, Ping |
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| score |
7.398961 |