東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Practical Appropriate Fidelity Optim...

Wu, Neil Y.

FindBook

Google Book

Amazon

博客來

Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design./
作者:	Wu, Neil Y.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	240 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30547496
ISBN:	9798379564841

Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design.
Wu, Neil Y.

Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 240 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of Michigan, 2023.

This item must not be sold to any third party vendors.

Numerical optimization has been successfully applied to multidisciplinary design optimizations such as aerostructural wing design. These optimizations consist of over a thousand design variables constraints, and include expensive simulations such as computational fluid dynamics within the optimization loop. Nevertheless, by using gradient-based optimizers together with efficient gradient computation techniques, researchers have been able to tackle these challenging large-scale problems. However, longstanding challenges remain. These optimizations tend to require direct user input, manually tuning various optimization parameters to obtain convergence. The optimizations are slow and computationally expensive, often using thousands of processors for several days at a time. As aircraft designers move toward using more expensive, higher-fidelity tools in design, the computational cost will only increase in the future. To address these challenges, this dissertation contains three main contributions. First, a novel geometric parameterization is presented. Based on sensitivity analysis, the parameterization is also able to automatically scale the newly-generated design variables, suitably for gradient-based optimization. This approach is demonstrated on two aerodynamic shape optimizations, and is shown to perform comparably to the manual approach requiring trial and error. Second, an adaptive error control scheme is developed to reduce the computational cost of optimizations. The function error due to convergence of the solver is estimated using an adjoint-derived approach. The error is then adapted during optimization, allowing for loose solver convergence at the beginning of the optimization, thereby reducing computational cost. The approach is demonstrated on two aerodynamic shape optimizations, showing between 30%-50% cost savings. Third, an appropriate fidelity optimization framework is developed, suited for gradient-based multidisciplinary design optimizations. This framework uses a sequential approach, and begins by quantifying the errors present in each fidelity at the discipline level. The errors are then propagated to the system-level objective and constraints. An optimization is started using the lowest fidelity, and these errors are used to terminate the low-fidelity optimizations at an appropriate time. The errors are also used to select the next appropriate fidelity, by performing a tradeoff between error reduction and computational cost increase. A new optimization is then started using the selected fidelity, and the process is repeated until the high-fidelity optimum is reached. The approach is then demonstrated on two aerostructural optimizations, including the XRF1 aircraft with over 900 design variables and 900 constraints. The aircraft is analyzed at four flight conditions, and there are a total of over 300,000 possible fidelity combinations. Through the use of the appropriate fidelity framework, cost savings of between 44% and 64% were realized.

ISBN: 9798379564841Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Design optimization

Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design.
LDR:04379nmm a2200397 4500 001 2397234
005 20240617111352.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379564841
035 $a (MiAaPQ)AAI30547496
035 $a (MiAaPQ)umichrackham004868
035 $a AAI30547496
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wu, Neil Y. $3 3766995
245 1 0 $a Practical Appropriate Fidelity Optimization for Large-Scale Multidisciplinary Aircraft Design.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 240 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Mader, Charles;Martins, Joaquim R.R.A.
502 $a Thesis (Ph.D.)--University of Michigan, 2023.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Numerical optimization has been successfully applied to multidisciplinary design optimizations such as aerostructural wing design. These optimizations consist of over a thousand design variables constraints, and include expensive simulations such as computational fluid dynamics within the optimization loop. Nevertheless, by using gradient-based optimizers together with efficient gradient computation techniques, researchers have been able to tackle these challenging large-scale problems. However, longstanding challenges remain. These optimizations tend to require direct user input, manually tuning various optimization parameters to obtain convergence. The optimizations are slow and computationally expensive, often using thousands of processors for several days at a time. As aircraft designers move toward using more expensive, higher-fidelity tools in design, the computational cost will only increase in the future. To address these challenges, this dissertation contains three main contributions. First, a novel geometric parameterization is presented. Based on sensitivity analysis, the parameterization is also able to automatically scale the newly-generated design variables, suitably for gradient-based optimization. This approach is demonstrated on two aerodynamic shape optimizations, and is shown to perform comparably to the manual approach requiring trial and error. Second, an adaptive error control scheme is developed to reduce the computational cost of optimizations. The function error due to convergence of the solver is estimated using an adjoint-derived approach. The error is then adapted during optimization, allowing for loose solver convergence at the beginning of the optimization, thereby reducing computational cost. The approach is demonstrated on two aerodynamic shape optimizations, showing between 30%-50% cost savings. Third, an appropriate fidelity optimization framework is developed, suited for gradient-based multidisciplinary design optimizations. This framework uses a sequential approach, and begins by quantifying the errors present in each fidelity at the discipline level. The errors are then propagated to the system-level objective and constraints. An optimization is started using the lowest fidelity, and these errors are used to terminate the low-fidelity optimizations at an appropriate time. The errors are also used to select the next appropriate fidelity, by performing a tradeoff between error reduction and computational cost increase. A new optimization is then started using the selected fidelity, and the process is repeated until the high-fidelity optimum is reached. The approach is then demonstrated on two aerostructural optimizations, including the XRF1 aircraft with over 900 design variables and 900 constraints. The aircraft is analyzed at four flight conditions, and there are a total of over 300,000 possible fidelity combinations. Through the use of the appropriate fidelity framework, cost savings of between 44% and 64% were realized.
590 $a School code: 0127.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Systems science. $3 3168411
653 $a Design optimization
653 $a Geometric parameterization
653 $a Multifidelity optimization
653 $a Adjoint-based error estimation
690 $a 0538
690 $a 0790
710 2 $a University of Michigan. $b Aerospace Engineering. $3 2093897
773 0 $t Dissertations Abstracts International $g 84-12B.
790 $a 0127
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30547496