Type of Document	Master's Thesis
Author	Fuller, Russell M.
URN	etd-09182008-063616
Title	Neural network estimation of disturbance growth and flow field structure of spatially excited jets
Degree	Master of Science
Department	Mechanical Engineering
Advisory Committee	Advisor Name Title Sauders, William Committee Chair Vandsburger, Uri Committee Member VanLandingham, Hugh F. Committee Member
Keywords	neural networks non-linear identification jet shear flows
Date of Defense	1996-06-15
Availability	restricted
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.
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