{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/44833"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/44833","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Neural network estimation of disturbance growth and flow field structure of spatially excited jets","abstract":"Neural 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.","abstract_html":"Neural 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.","abstract_has_math":false,"creators":["Fuller, Russell M."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Saunders, William R."],"committee_members":["Vandsburger, Uri","VanLandingham, Hugh F."],"year":1996,"date_issued":"1996-06-15","date_published":"1996-06-15","updated_at":"2026-07-22T22:19:24Z","subjects":["neural networks","non-linear identification","jet shear flows"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-09182008-063616"],"render_values":[{"text":"etd-09182008-063616","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/44833","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Saunders, William R."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Vandsburger, Uri","VanLandingham, Hugh F."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Fuller, Russell M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T21:46:05Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T21:46:05Z","2008-09-18"]},{"key":"dc:date.issued","label":"Date","values":["1996-06-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["neural networks","non-linear identification","jet shear flows"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-09182008-063616"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/44833"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Neural networks were applied to the estimation problem consisting of identifying both nearfield and quasi-farfield flow structures of a jet undergoing spatial mode excitation. 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