Sequentially Optimized Meshfree Approximation as a New Computation Fluid Dynamics Method

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dc.contributor.advisorMeade, Andrew J., Jr.en_US
dc.contributor.committeeMemberAkin, John Edward.en_US
dc.contributor.committeeMemberEmbree, Marken_US
dc.contributor.committeeMemberHouchens, Brent C.en_US
dc.creatorWilkinson, Matthewen_US
dc.date.accessioned2012-09-06T04:25:35Zen_US
dc.date.accessioned2012-09-06T04:25:46Zen_US
dc.date.available2012-09-06T04:25:35Zen_US
dc.date.available2012-09-06T04:25:46Zen_US
dc.date.created2012-05en_US
dc.date.issued2012-09-05en_US
dc.date.submittedMay 2012en_US
dc.date.updated2012-09-06T04:25:47Zen_US
dc.description.abstractThis thesis presents the Sequentially Optimized Meshfree Approximation (SOMA) method, a new and powerful Computational Fluid Dynamics (CFD) solver. While standard computational methods can be faster and cheaper that physical experimentation, both in cost and work time, these methods do have some time and user interaction overhead which SOMA eliminates. As a meshfree method which could use adaptive domain refinement methods, SOMA avoids the need for user generated and/or analyzed grids, volumes, and meshes. Incremental building of a feed-forward artificial neural network through machine learning to solve the flow problem significantly reduces user interaction and reduces computational cost. This is done by avoiding the creation and inversion of possibly dense block diagonal matrices and by focusing computational work on regions where the flow changes and ignoring regions where no changes occur.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationWilkinson, Matthew. "Sequentially Optimized Meshfree Approximation as a New Computation Fluid Dynamics Method." (2012) Diss., Rice University. <a href="https://hdl.handle.net/1911/64673">https://hdl.handle.net/1911/64673</a>.en_US
dc.identifier.slug123456789/ETD-2012-05-138en_US
dc.identifier.urihttps://hdl.handle.net/1911/64673en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectAerospace engineeringen_US
dc.subjectComputational fluid dynamicsen_US
dc.subjectMechanical engineeringen_US
dc.subjectMeshfree methodsen_US
dc.subjectSequential optimizationen_US
dc.subjectNeural networksen_US
dc.titleSequentially Optimized Meshfree Approximation as a New Computation Fluid Dynamics Methoden_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMechanical Engineering and Materials Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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