William A. Crossley

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A parallel genetic algorithm (GA) was used to generate, in a single run, a family of aerodynamically efficient, low-noise rotor blade designs representing the Pareto optimal set. The n-branch tournament, uniform crossover genetic algorithm operates on twenty design variables, which constitute the control points for a spline representing the airfoil surface.(More)
Constrained optimization via the Genetic Algorithm (GA) is often a challenging endeavor, as the GA is most directly suited to unconstrained optimization. Traditionally, external penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. This approach requires the somewhat arbitrary(More)
Morphing aircraft are multi-role aircraft that use innovative actuators, effectors, and mechanisms to change their state to perform select missions with substantially improved system performance. State change in this study means a change in the cross-sectional shape of the wing itself, not chord extension or span extension. Integrating actuators and(More)
The capabilities of genetic algorithms as a non-c'alculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of(More)
Recently, there has been great interest in developing technologies that may enable a morphing aircraft. Such an aircraft can change shape in flight, which would make it possible to adjust the wing to the best possible shape for any flight condition encountered by the aircraft. However, there is an actuation cost associated with making these shape changes(More)
The aircraft design optimization problem is typically formulated to maximize performance at a small number of representative operating conditions. This approach makes simplifying assumptions such as ignoring the climb and descent phases, but they can be avoided by performing simultaneous design-mission-allocation optimization with surrogate models for the(More)