國立虎尾科技大學 |

Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty./
作者:	Bedonian, Garo.
面頁冊數:	1 online resource (136 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Statistical physics. -
電子資源:	click for full text (PQDT)
ISBN:	9798383058978

Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty.
Bedonian, Garo.

Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty. - 1 online resource (136 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.

Includes bibliographical references

Engineers are interested in numerical robust design optimization (RDO) during early phases of the design process for its ability to produce system configurations that are both optimally performant and robust to sources of uncertainty in fabrication, operation, and analysis. Prohibitive to this practice is the often intractable cost of accurately estimating statistical or probabilistic measures of the optimization objectives or constraints from repeated sampling of analysis codes, particularly for problems involving expensive high-fidelity or multi-disciplinary analyses. Given the recent proliferation of efficient gradient calculation methods within these analysis codes, we explore and contribute to gradient-based methods for accelerating robust design optimization, simultaneously leveraging gradient-enhanced uncertainty quantification (UQ) and gradient-based optimization techniques. To this end, we develop a novel gradient-based partition-of-unity surrogate model and adaptive sampling method tailored to robust design optimization. Furthermore, we propose a multi-fidelity robust optimization method that uses surrogate-based UQ and adaptive sampling-based gradient error estimates to mitigate the cost of sampling effort as the optimizer approaches convergence. For a suite of analytical test problems and an aerostructural design problem involving uncertainties related to shock-boundary layer interaction, the novel surrogate and adaptive sampling method demonstrate competitive to superior global accuracy per sample compared to standard surrogates, and the proposed multi-fidelity optimization method demonstrates greatly-reduced sampling effort to achieve design convergence overall when compared to single-fidelity benchmarks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058978Subjects--Topical Terms:

528048
Statistical physics.
Subjects--Index Terms:

Design optimizationIndex Terms--Genre/Form:

554714
Electronic books.

Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty.
LDR:03181ntm a22004097 4500 001 1149710
005 20241022112620.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383058978
035 $a (MiAaPQ)AAI31144645
035 $a AAI31144645
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Bedonian, Garo. $3 1476042
245 1 0 $a Gradient-Enhanced Robust Design Optimization for Engineering Systems Under Uncertainty.
264 0 $c 2024
300 $a 1 online resource (136 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Hicken, Jason E.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.
504 $a Includes bibliographical references
520 $a Engineers are interested in numerical robust design optimization (RDO) during early phases of the design process for its ability to produce system configurations that are both optimally performant and robust to sources of uncertainty in fabrication, operation, and analysis. Prohibitive to this practice is the often intractable cost of accurately estimating statistical or probabilistic measures of the optimization objectives or constraints from repeated sampling of analysis codes, particularly for problems involving expensive high-fidelity or multi-disciplinary analyses. Given the recent proliferation of efficient gradient calculation methods within these analysis codes, we explore and contribute to gradient-based methods for accelerating robust design optimization, simultaneously leveraging gradient-enhanced uncertainty quantification (UQ) and gradient-based optimization techniques. To this end, we develop a novel gradient-based partition-of-unity surrogate model and adaptive sampling method tailored to robust design optimization. Furthermore, we propose a multi-fidelity robust optimization method that uses surrogate-based UQ and adaptive sampling-based gradient error estimates to mitigate the cost of sampling effort as the optimizer approaches convergence. For a suite of analytical test problems and an aerostructural design problem involving uncertainties related to shock-boundary layer interaction, the novel surrogate and adaptive sampling method demonstrate competitive to superior global accuracy per sample compared to standard surrogates, and the proposed multi-fidelity optimization method demonstrates greatly-reduced sampling effort to achieve design convergence overall when compared to single-fidelity benchmarks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Statistical physics. $3 528048
650 4 $a Computational physics. $3 1181955
650 4 $a Computer engineering. $3 569006
650 4 $a Mechanical engineering. $3 557493
653 $a Design optimization
653 $a Gradient-enhanced
653 $a Multi-fidelity
653 $a Surrogate modeling
653 $a Uncertainty quantification
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
690 $a 0464
690 $a 0216
690 $a 0217
710 2 $a Rensselaer Polytechnic Institute. $b Mechanical Engineering. $3 1178915
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31144645 $z click for full text (PQDT)