Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure

Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in o...
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Gespeichert in:

Autor*in:	Salas, Pablo [verfasserIn] Venkataraman, Satchi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Robust design Model uncertainty Progressive failure

Anmerkung:	© Springer-Verlag 2008

Übergeordnetes Werk:	Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 37(2008), 6 vom: 30. Mai
Übergeordnetes Werk:	volume:37 ; year:2008 ; number:6 ; day:30 ; month:05

Links:	Volltext

DOI / URN:	10.1007/s00158-008-0259-y

Katalog-ID:	SPR00131095X

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allfields	10.1007/s00158-008-0259-y doi (DE-627)SPR00131095X (SPR)s00158-008-0259-y-e DE-627 ger DE-627 rakwb eng Salas, Pablo verfasserin aut Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213 Venkataraman, Satchi aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 37(2008), 6 vom: 30. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:37 year:2008 number:6 day:30 month:05 https://dx.doi.org/10.1007/s00158-008-0259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2008 6 30 05
spelling	10.1007/s00158-008-0259-y doi (DE-627)SPR00131095X (SPR)s00158-008-0259-y-e DE-627 ger DE-627 rakwb eng Salas, Pablo verfasserin aut Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213 Venkataraman, Satchi aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 37(2008), 6 vom: 30. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:37 year:2008 number:6 day:30 month:05 https://dx.doi.org/10.1007/s00158-008-0259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2008 6 30 05
allfields_unstemmed	10.1007/s00158-008-0259-y doi (DE-627)SPR00131095X (SPR)s00158-008-0259-y-e DE-627 ger DE-627 rakwb eng Salas, Pablo verfasserin aut Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213 Venkataraman, Satchi aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 37(2008), 6 vom: 30. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:37 year:2008 number:6 day:30 month:05 https://dx.doi.org/10.1007/s00158-008-0259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2008 6 30 05
allfieldsGer	10.1007/s00158-008-0259-y doi (DE-627)SPR00131095X (SPR)s00158-008-0259-y-e DE-627 ger DE-627 rakwb eng Salas, Pablo verfasserin aut Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213 Venkataraman, Satchi aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 37(2008), 6 vom: 30. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:37 year:2008 number:6 day:30 month:05 https://dx.doi.org/10.1007/s00158-008-0259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2008 6 30 05
allfieldsSound	10.1007/s00158-008-0259-y doi (DE-627)SPR00131095X (SPR)s00158-008-0259-y-e DE-627 ger DE-627 rakwb eng Salas, Pablo verfasserin aut Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213 Venkataraman, Satchi aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 37(2008), 6 vom: 30. Mai (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:37 year:2008 number:6 day:30 month:05 https://dx.doi.org/10.1007/s00158-008-0259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2008 6 30 05
language	English
source	Enthalten in Structural and multidisciplinary optimization 37(2008), 6 vom: 30. Mai volume:37 year:2008 number:6 day:30 month:05
sourceStr	Enthalten in Structural and multidisciplinary optimization 37(2008), 6 vom: 30. Mai volume:37 year:2008 number:6 day:30 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Robust design Model uncertainty Progressive failure
isfreeaccess_bool	false
container_title	Structural and multidisciplinary optimization
authorswithroles_txt_mv	Salas, Pablo @@aut@@ Venkataraman, Satchi @@aut@@
publishDateDaySort_date	2008-05-30T00:00:00Z
hierarchy_top_id	271602503
id	SPR00131095X
language_de	englisch
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author	Salas, Pablo
spellingShingle	Salas, Pablo misc Robust design misc Model uncertainty misc Progressive failure Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
authorStr	Salas, Pablo
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topic_title	Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure Robust design (dpeaa)DE-He213 Model uncertainty (dpeaa)DE-He213 Progressive failure (dpeaa)DE-He213
topic	misc Robust design misc Model uncertainty misc Progressive failure
topic_unstemmed	misc Robust design misc Model uncertainty misc Progressive failure
topic_browse	misc Robust design misc Model uncertainty misc Progressive failure
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title	Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
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title_full	Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
author_sort	Salas, Pablo
journal	Structural and multidisciplinary optimization
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author_browse	Salas, Pablo Venkataraman, Satchi
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doi_str_mv	10.1007/s00158-008-0259-y
title_sort	laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
title_auth	Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
abstract	Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. © Springer-Verlag 2008
abstractGer	Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. © Springer-Verlag 2008
abstract_unstemmed	Abstract Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization. © Springer-Verlag 2008
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title_short	Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure
url	https://dx.doi.org/10.1007/s00158-008-0259-y
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up_date	2024-07-03T21:42:24.007Z
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