Uncertainty quantification tools for multiphase gas-solid flow simulations using MFIX

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dc.contributor.advisor Rodney O. Fox
dc.contributor.advisor Alberto Passalacqua
dc.contributor.author Hu, Xiaofei
dc.contributor.department Chemical and Biological Engineering
dc.date 2018-08-11T19:21:27.000
dc.date.accessioned 2020-06-30T02:55:00Z
dc.date.available 2020-06-30T02:55:00Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2014
dc.date.embargo 2001-01-01
dc.date.issued 2014-01-01
dc.description.abstract <p>Computational fluid dynamics (CFD) has been widely studied and used in the scientific community and in the industry. Various models were proposed to solve problems in different areas. However, all models deviate from reality. Uncertainty quantification (UQ) process evaluates the overall uncertainties associated with the prediction of quantities of interest. In particular it studies the propagation of input uncertainties to the outputs of the models so that confidence intervals can be provided for the simulation results. In the present work, a non-intrusive quadrature-based uncertainty quantification (QBUQ) approach is proposed. The probability distribution function (PDF) of the system response can be then reconstructed using extended quadrature method of moments (EQMOM) and extended conditional quadrature method of moments (ECQMOM). The method is first illustrated considering two examples: a developing flow in a channel with uncertain viscosity, and an oblique shock problem with uncertain upstream Mach number. The error in the prediction of the moment response is studied as a function of the number of samples, and the accuracy of the moments required to reconstruct the PDF of the system response is discussed. The approach proposed in this work is then demonstrated by considering a bubbling fluidized bed as example application. The mean particle size is assumed to be the uncertain input parameter. The system is simulated with a standard two-fluid model with kinetic theory closures for the particulate phase implemented into MFIX. The effect of uncertainty on the disperse-phase volume fraction, on the phase velocities and on the pressure drop inside the fluidized bed are examined, and the reconstructed PDFs are provided for the three</p> <p>quantities studied. Then the approach is applied to a bubbling fluidized bed with two uncertain parameters. Contour plots of the mean and standard deviation of solid volume fraction, solid phase velocities and gas pressure are provided. The PDFs of the response are reconstructed using EQMOM with appropriate kernel density functions. The simulation results are compared to experimental data provided by the 2013 NETL small-scale challenge problem. Lastly, the proposed procedure is demonstrated by considering a riser of a circulating fluidized bed as an example application. The mean particle size is considered to be the uncertain input parameters. Contour plots of the mean and standard deviation of solid volume fraction, solid phase velocities, and granular temperature are provided. Mean values and confidence intervals of the quantities of interest are compared to the experiment results. The univariate and bivariate PDF reconstructions of the system response are performed using EQMOM and ECQMOM.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/14192/
dc.identifier.articleid 5199
dc.identifier.contextkey 7766133
dc.identifier.doi https://doi.org/10.31274/etd-180810-3738
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/14192
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/28378
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/14192/Hu_iastate_0097E_14549.pdf|||Fri Jan 14 20:15:45 UTC 2022
dc.subject.disciplines Chemical Engineering
dc.subject.keywords Chemical Engineering
dc.subject.keywords conditional quadrature method of moments (CQMOM)
dc.subject.keywords extended quadrature method of moments (EQMOM)
dc.subject.keywords multiphase gas-solid flow
dc.subject.keywords non-intrusive uncertainty quantification
dc.subject.keywords pdf reconstruction
dc.subject.keywords quadrature-based uncertainty quantification
dc.title Uncertainty quantification tools for multiphase gas-solid flow simulations using MFIX
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 86545861-382c-4c15-8c52-eb8e9afe6b75
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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