Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor
An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves mu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Pengcheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
Analytic target cascading (ATC) Uncertainty-based multi-disciplinary design optimization (UMDO) |
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| Übergeordnetes Werk: |
Enthalten in: Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience - Baysal, Birol ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:114 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.ast.2021.106680 |
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ELV053996887 |
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| 245 | 1 | 0 | |a Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor |
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| 520 | |a An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. | ||
| 520 | |a An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. | ||
| 650 | 7 | |a Launch vehicle (LV) |2 Elsevier | |
| 650 | 7 | |a Analytic target cascading (ATC) |2 Elsevier | |
| 650 | 7 | |a Hybrid rocket motor (HRM) |2 Elsevier | |
| 650 | 7 | |a Sensitivity analysis (SA) |2 Elsevier | |
| 650 | 7 | |a Fuzzy theory |2 Elsevier | |
| 650 | 7 | |a Uncertainty-based multi-disciplinary design optimization (UMDO) |2 Elsevier | |
| 700 | 1 | |a Zhu, Hao |4 oth | |
| 700 | 1 | |a Tian, Hui |4 oth | |
| 700 | 1 | |a Cai, Guobiao |4 oth | |
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10.1016/j.ast.2021.106680 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001388.pica (DE-627)ELV053996887 (ELSEVIER)S1270-9638(21)00190-5 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Wang, Pengcheng verfasserin aut Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. Launch vehicle (LV) Elsevier Analytic target cascading (ATC) Elsevier Hybrid rocket motor (HRM) Elsevier Sensitivity analysis (SA) Elsevier Fuzzy theory Elsevier Uncertainty-based multi-disciplinary design optimization (UMDO) Elsevier Zhu, Hao oth Tian, Hui oth Cai, Guobiao oth Enthalten in Elsevier Science Baysal, Birol ELSEVIER Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience 2015 Amsterdam [u.a.] (DE-627)ELV013466232 volume:114 year:2021 pages:0 https://doi.org/10.1016/j.ast.2021.106680 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 114 2021 0 |
| spelling |
10.1016/j.ast.2021.106680 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001388.pica (DE-627)ELV053996887 (ELSEVIER)S1270-9638(21)00190-5 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Wang, Pengcheng verfasserin aut Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. Launch vehicle (LV) Elsevier Analytic target cascading (ATC) Elsevier Hybrid rocket motor (HRM) Elsevier Sensitivity analysis (SA) Elsevier Fuzzy theory Elsevier Uncertainty-based multi-disciplinary design optimization (UMDO) Elsevier Zhu, Hao oth Tian, Hui oth Cai, Guobiao oth Enthalten in Elsevier Science Baysal, Birol ELSEVIER Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience 2015 Amsterdam [u.a.] (DE-627)ELV013466232 volume:114 year:2021 pages:0 https://doi.org/10.1016/j.ast.2021.106680 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 114 2021 0 |
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10.1016/j.ast.2021.106680 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001388.pica (DE-627)ELV053996887 (ELSEVIER)S1270-9638(21)00190-5 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Wang, Pengcheng verfasserin aut Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. Launch vehicle (LV) Elsevier Analytic target cascading (ATC) Elsevier Hybrid rocket motor (HRM) Elsevier Sensitivity analysis (SA) Elsevier Fuzzy theory Elsevier Uncertainty-based multi-disciplinary design optimization (UMDO) Elsevier Zhu, Hao oth Tian, Hui oth Cai, Guobiao oth Enthalten in Elsevier Science Baysal, Birol ELSEVIER Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience 2015 Amsterdam [u.a.] (DE-627)ELV013466232 volume:114 year:2021 pages:0 https://doi.org/10.1016/j.ast.2021.106680 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 114 2021 0 |
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10.1016/j.ast.2021.106680 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001388.pica (DE-627)ELV053996887 (ELSEVIER)S1270-9638(21)00190-5 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Wang, Pengcheng verfasserin aut Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. Launch vehicle (LV) Elsevier Analytic target cascading (ATC) Elsevier Hybrid rocket motor (HRM) Elsevier Sensitivity analysis (SA) Elsevier Fuzzy theory Elsevier Uncertainty-based multi-disciplinary design optimization (UMDO) Elsevier Zhu, Hao oth Tian, Hui oth Cai, Guobiao oth Enthalten in Elsevier Science Baysal, Birol ELSEVIER Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience 2015 Amsterdam [u.a.] (DE-627)ELV013466232 volume:114 year:2021 pages:0 https://doi.org/10.1016/j.ast.2021.106680 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 114 2021 0 |
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10.1016/j.ast.2021.106680 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001388.pica (DE-627)ELV053996887 (ELSEVIER)S1270-9638(21)00190-5 DE-627 ger DE-627 rakwb eng 610 VZ 600 670 VZ 51.00 bkl Wang, Pengcheng verfasserin aut Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. Launch vehicle (LV) Elsevier Analytic target cascading (ATC) Elsevier Hybrid rocket motor (HRM) Elsevier Sensitivity analysis (SA) Elsevier Fuzzy theory Elsevier Uncertainty-based multi-disciplinary design optimization (UMDO) Elsevier Zhu, Hao oth Tian, Hui oth Cai, Guobiao oth Enthalten in Elsevier Science Baysal, Birol ELSEVIER Mo1474 The Role of EUS Examination and EUS-Guided Fine Needle Aspiration Biopsy for Evaluation of Gastric Subepithelial Lesions: a Large Single Center Experience 2015 Amsterdam [u.a.] (DE-627)ELV013466232 volume:114 year:2021 pages:0 https://doi.org/10.1016/j.ast.2021.106680 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 114 2021 0 |
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analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor |
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Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor |
| abstract |
An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. |
| abstractGer |
An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. |
| abstract_unstemmed |
An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method. |
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