Neural network estimation of disturbance growth and flow field structure of spatially excited jets

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dc.contributor.authorFuller, Russell M.en
dc.contributor.committeechairSaunders, William R.en
dc.contributor.committeememberVandsburger, Urien
dc.contributor.committeememberVanLandingham, Hugh F.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T21:46:05Zen
dc.date.adate2008-09-18en
dc.date.available2014-03-14T21:46:05Zen
dc.date.issued1996-06-15en
dc.date.rdate2008-09-18en
dc.date.sdate2008-09-18en
dc.description.abstractNeural networks were applied to the estimation problem consisting of identifying both nearfield and quasi-farfield flow structures of a jet undergoing spatial mode excitation. The evolution of disturbances introduced by a spatially excited jet spans a linear and nonlinear regime in the downstream flow field. For the linear portion, the neural network was trained to identify critical flow field parameters using numerical data generated from linear stability analysis code. It was shown that the neural network could function as a multiple-input adaptive linear combiner over the linear nearfield of the jet flowfield. Beyond the nearfield (2.0 ≤ z/D ≤ 6.0), a back propagation neural network was trained using experimental data captured during different modal excitation patterns. Constant velocity contours for mode 0, mode 1, mode ±1, and mode ±2 jet excitations were accurately estimated using a low-order neural network filter with conditioned inputs. Moderate success was also demonstrated when the network was used to extrapolate flow field parameters outside the initial training set. This demonstration of using neural networks to predict flowfield structure in non-reacting flows is expected to be directly applicable to estimation and control of reacting flows in combustors.en
dc.description.degreeMaster of Scienceen
dc.format.extentxi, 126 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-09182008-063616en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09182008-063616/en
dc.identifier.urihttp://hdl.handle.net/10919/44833en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1996.F855.pdfen
dc.relation.isformatofOCLC# 35332692en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectneural networksen
dc.subjectnon-linear identificationen
dc.subjectjet shear flowsen
dc.subject.lccLD5655.V855 1996.F855en
dc.titleNeural network estimation of disturbance growth and flow field structure of spatially excited jetsen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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