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On Improving the Predictable Accurac...

Lee, Michael W.

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On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows.

Record Type:	Electronic resources : Monograph/item
Title/Author:	On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows./
Author:	Lee, Michael W.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	154 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-12.
Contained By:	Dissertations Abstracts International81-12.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27741982
ISBN:	9798645465926

On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows.
Lee, Michael W.

On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 154 p.

Source: Dissertations Abstracts International, Volume: 81-12.

Thesis (Ph.D.)--Duke University, 2020.

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

The proper orthogonal decomposition (POD) is a classic method to construct empirical, linear modal bases which are optimal in a mean L2 sense. A subset of these modes can form the basis of a dynamical reduced-order model (ROM) of a physical system, including nonlinear, chaotic systems like fluid flows. While these POD-based ROMs can accurately simulate complex fluid dynamics, a priori model accuracy and stability estimates are unreliable. The work presented in this dissertation focuses on improving the predictability and accuracy of POD-based fluid ROMs. This is accomplished by ensuring several kinematically significant flow characteristics - both at large scales and small - are satisfied within the truncated bases. Several new methods of constructing and employing modal bases within this context are developed and tested. Reduced-order models of periodic flows are shown to be predictably accurate with high confidence; the predictable accuracy of quasi-periodic and chaotic fluid flow ROMs is increased significantly relative to existing approaches.

ISBN: 9798645465926Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Nonlinear dynamics

On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows.
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100 1 $a Lee, Michael W. $3 3550503
245 1 0 $a On Improving the Predictable Accuracy of Reduced-Order Models for Fluid Flows.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 154 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-12.
500 $a Advisor: Dowell, Earl H.
502 $a Thesis (Ph.D.)--Duke University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a The proper orthogonal decomposition (POD) is a classic method to construct empirical, linear modal bases which are optimal in a mean L2 sense. A subset of these modes can form the basis of a dynamical reduced-order model (ROM) of a physical system, including nonlinear, chaotic systems like fluid flows. While these POD-based ROMs can accurately simulate complex fluid dynamics, a priori model accuracy and stability estimates are unreliable. The work presented in this dissertation focuses on improving the predictability and accuracy of POD-based fluid ROMs. This is accomplished by ensuring several kinematically significant flow characteristics - both at large scales and small - are satisfied within the truncated bases. Several new methods of constructing and employing modal bases within this context are developed and tested. Reduced-order models of periodic flows are shown to be predictably accurate with high confidence; the predictable accuracy of quasi-periodic and chaotic fluid flow ROMs is increased significantly relative to existing approaches.
590 $a School code: 0066.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Fluid mechanics. $3 528155
650 4 $a Computational physics. $3 3343998
653 $a Nonlinear dynamics
653 $a Proper orthogonal decomposition
653 $a Reduced-order modeling
653 $a Turbulence
690 $a 0548
690 $a 0204
690 $a 0216
710 2 $a Duke University. $b Mechanical Engineering and Materials Science. $3 1030656
773 0 $t Dissertations Abstracts International $g 81-12.
790 $a 0066
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27741982