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A Novel Smoother-Based Data Assimila...

Hurst, Christopher L.,

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A Novel Smoother-Based Data Assimilation Method for Complex CFD /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Novel Smoother-Based Data Assimilation Method for Complex CFD // Christopher L Hurst.
作者:	Hurst, Christopher L.,
面頁冊數:	1 electronic resource (113 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31142985
ISBN:	9798382725765

A Novel Smoother-Based Data Assimilation Method for Complex CFD /
Hurst, Christopher L.,

A Novel Smoother-Based Data Assimilation Method for Complex CFD /Christopher L Hurst. - 1 electronic resource (113 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Accurate computational fluid dynamics (CFD) modeling of turbulent flows is necessary for improving fluid-driven engineering designs. Traditional CFD often falls short of providing truly accurate solutions due to inherent uncertainties stemming from modeling assumptions and the chaotic nature of fluid flow. To overcome these limitations, we propose the integration of data assimilation (DA) techniques into CFD simulations. DA, which incorporates observational data into numerical models, offers a promising avenue to enhance predictability by reducing uncertainties associated with initial conditions and model parameters. This research aims to advance our understanding and application of DA for CFD modeling of highly chaotic dynamical systems. This dissertation makes several novel contributions in DA and CFD: i) A novel DA algorithm, the maximum likelihood ensemble smoother (MLES), has been developed and implemented to provide better model parameter estimation and assimilate time-integrated observations while addressing nonlinearity, ii) Multigrid-in-time techniques are applied to enhance the computational efficiency of the MLES by improving the optimization processes, and iii) The MLES+CFD framework has been validated by classical test problems such as the Lorenz 96 model and the Kuramoto-Sivashinsky equation. The effectiveness of the MLES has been demonstrated through a few test problems featuring chaos, discontinuity, or high dimensionality.

English

ISBN: 9798382725765Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Computational fluid dynamics

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520 $a Accurate computational fluid dynamics (CFD) modeling of turbulent flows is necessary for improving fluid-driven engineering designs. Traditional CFD often falls short of providing truly accurate solutions due to inherent uncertainties stemming from modeling assumptions and the chaotic nature of fluid flow. To overcome these limitations, we propose the integration of data assimilation (DA) techniques into CFD simulations. DA, which incorporates observational data into numerical models, offers a promising avenue to enhance predictability by reducing uncertainties associated with initial conditions and model parameters. This research aims to advance our understanding and application of DA for CFD modeling of highly chaotic dynamical systems. This dissertation makes several novel contributions in DA and CFD: i) A novel DA algorithm, the maximum likelihood ensemble smoother (MLES), has been developed and implemented to provide better model parameter estimation and assimilate time-integrated observations while addressing nonlinearity, ii) Multigrid-in-time techniques are applied to enhance the computational efficiency of the MLES by improving the optimization processes, and iii) The MLES+CFD framework has been validated by classical test problems such as the Lorenz 96 model and the Kuramoto-Sivashinsky equation. The effectiveness of the MLES has been demonstrated through a few test problems featuring chaos, discontinuity, or high dimensionality.
546 $a English
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650 4 $a Mechanical engineering. $3 649730
650 4 $a Fluid mechanics. $3 528155
650 4 $a Computational physics. $3 3343998
653 $a Computational fluid dynamics
653 $a Computational mathematics
653 $a Data assimilation
653 $a High performance computing
653 $a Engineering designs
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710 2 $a Colorado State University. $b Mechanical Engineering. $e degree granting institution. $3 3821970
720 1 $a Gao, Xinfeng $e degree supervisor.
720 1 $a Guzik, Stephen $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
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791 $a Ph.D.
792 $a 2024
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