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Themistoklis P. Sapsis. Charalampopoulos, Alexis-Tzianni. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2020-02-10T21:42:06Z 2020-02-10T21:42:06Z 2019 2019 https://hdl.handle.net/1721.1/123757 1138990951 Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-68). In this thesis we formulate a data-driven, nonlocal closure scheme for turbulent multiphase fluid flows. In more detail, we use the predictions of neural nets to actively integrate in time the evolution of a turbulent anisotropic fluid flow on which bubbles that act as passive inertial tracers are being transported. The first step of our method requires the introduction of a filter on the initial 2D dynamical system that reduces it to a 1D problem. Then we model the appearing closure terms using recurrent neural networks and convolutions in space. We avoid the use of fully-connected layers due to their large computational overhead, tendency to over-fit on training data and nonphysical implications. We choose to work with recurrent neural networks as recent works have shown memory effects can enhance data-driven predictions in applications in turbulence. We first test our method on unimodal jets and then proceed to bimodal profiles. Our model appears to accurately learn the statistical steady state profile of both the fluid flow velocity field as well as the profile of the distribution of bubbles in space. Finally, we use neural networks to learn the statistics of bubble deformation so that we can incorporate the effects of the bubble motion back to the fluid flow itself, on a later stage. by Alexis-Tzianni Charalampopoulos. S.M. S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering 68 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Machine learning non-local closures for turbulent anisotropic multiphase flows Thesis application/pdf 2020-02-10T21:42:05Z Publication Master MechE unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8de3f35f-2d23-4a37-b9b4-d5f4c2ffa40c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Machine learning non-local closures for turbulent anisotropic multiphase flows</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Charalampopoulos, Alexis-Tzianni.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>In this thesis we formulate a data-driven, nonlocal closure scheme for turbulent multiphase fluid flows. In more detail, we use the predictions of neural nets to actively integrate in time the evolution of a turbulent anisotropic fluid flow on which bubbles that act as passive inertial tracers are being transported. The first step of our method requires the introduction of a filter on the initial 2D dynamical system that reduces it to a 1D problem. Then we model the appearing closure terms using recurrent neural networks and convolutions in space. We avoid the use of fully-connected layers due to their large computational overhead, tendency to over-fit on training data and nonphysical implications. We choose to work with recurrent neural networks as recent works have shown memory effects can enhance data-driven predictions in applications in turbulence. We first test our method on unimodal jets and then proceed to bimodal profiles. Our model appears to accurately learn the statistical steady state profile of both the fluid flow velocity field as well as the profile of the distribution of bubbles in space. Finally, we use neural networks to learn the statistics of bubble deformation so that we can incorporate the effects of the bubble motion back to the fluid flow itself, on a later stage.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>