Date of Award
Master of Science
Department of Aeronautics and Astronautics
Jacob Freeman, PhD.
Optimization under uncertainty is performed to determine the optimal parameters of an active flow control jet to impart robust control during transonic cruise. A steady blowing jet is optimized on an airfoil to impart a change in lift greater than or equal to that generated by traditional control surfaces. The design candidates are computationally evaluated using the NASA flow solver, FUN3D, under 20 unique combinations of angle of attack, Reynolds number, and Mach number in order to propagate model input uncertainty. The mean change in lift and the associated standard deviation are included in the optimization framework to help ensure a robust solution. The mass flow rate required to achieve robust control is minimized. Due to time constraints, the optimization failed to produce an optimum solution. However, a number of designs produced an acceptable change in lift to theoretically control an aircraft. One design required a coefficient of mass flow rate of just 1.76E-3. Total predictive uncertainty is estimated as ±40.9% , of which ±8.8% is attributed to input uncertainty, ±13.8% to numerical uncertainty, and ±18.3% to model form uncertainty.
DTIC Accession Number
Welch, Luke A., "Computational Optimization Under Uncertainty of an Active Flow Control Jet" (2017). Theses and Dissertations. 1730.