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A novel smoother-based data assimilation method for complex CFD

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dc.contributor.authorHurst, Christopher L., author
dc.contributor.authorGao, Xinfeng, advisor
dc.contributor.authorGuzik, Stephen, advisor
dc.contributor.authorTroxell, Wade, committee member
dc.contributor.authorvan Leeuwen, Peter Jan, committee member
dc.date.accessioned2024-05-27T10:32:57Z
dc.date.available2024-05-27T10:32:57Z
dc.date.issued2024
dc.description.abstractAccurate 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.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierHurst_colostate_0053A_18357.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238530
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectcomputational mathematics
dc.subjecthigh performance computing
dc.subjectdata assimilation
dc.subjectcomputational fluid dynamics
dc.titleA novel smoother-based data assimilation method for complex CFD
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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