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dc.contributor.authorFuller, Russell M.en_US

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.

dc.publisherVirginia Techen_US
dc.subjectneural networksen_US
dc.subjectnon-linear identificationen_US
dc.subjectjet shear flowsen_US
dc.subject.lccLD5655.V855 1996.F855en_US
dc.titleNeural network estimation of disturbance growth and flow field structure of spatially excited jetsen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US of Scienceen_US Polytechnic Institute and State Universityen_US Engineeringen_US
dc.contributor.committeechairSaunders, William R.en_US
dc.contributor.committeememberVandsburger, Urien_US
dc.contributor.committeememberVanLandingham, Hugh F.en_US

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