Aeroelastic aspects of unmanned aerial vehicles (UAVs) is analyzed by treatment of a typical section containing geometrical nonlinearities. Equations of motion are derived and numerical integration of these equations subject to quasi-steady aerodynamic forcing is performed. Model properties are tailored to a high-altitude long-endurance unmanned aircraft. Harmonic balance approximation is employed based on the steady-state oscillatory response of the aerodynamic forcing. Comparisons are made between time integration results and harmonic balance approximation. Close agreement between forcing and displacement oscillatory frequencies is found. Amplitude agreement is off by a considerable margin. Additionally, stochastic forcing effects are examined. Turbulent flow velocities generated from the von Karman spectrum are applied to the same nonlinear structural model. Similar qualitative behavior is found between quasi-steady and stochastic forcing models illustrating the importance of considering the non-steady nature of atmospheric turbulence when operating near critical flutter velocity.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographical references (p. 96-98).

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This thesis presents the work of two significant projects. In the first project, a suite of benchmark problems for grid energy management are presented which demonstrate several issues characteristic to the dynamic optimization of these systems. These benchmark problems include load following, cogeneration, tri-generation, and energy storage, and each one assumes perfect foresight of the entire time horizon. The Gekko Python package for dynamic optimization is introduced and two different solution methods are discussed and applied to solving these benchmarks. The simultaneous solve mode out-performs the sequential solve mode in each benchmark problem across a wide range of time horizons with increasing resolution, demonstrating the ability of the simultaneous mode to handle many degrees of freedom across a range of problems of increasing difficulty. In the second project, combined optimization of propulsion system design, flight trajectory planning and battery mass optimization is applied to solar-regenerative high-altitude long-endurance (SR-HALE) aircraft through a sequential iterative approach. This combined optimization approach yields an increase of 20.2% in the end-of-day energy available on the winter solstice at 35°N latitude, resulting in an increase in flight time of 2.36 hours. The optimized flight path is obtained by using nonlinear model predictive control to solve flight and energy system dynamics over a 24 hour period with a 15 second time resolution. The optimization objective is to maximize the total energy in the system while flying a station-keeping mission, staying within a 3 km radius and above 60,000 ft. The propulsion system design optimization minimizes the total energy required to fly the optimal path. It uses a combination of blade element momentum theory, blade composite structures, empirical motor and motor controller mass data, as well as a first order motor performance model. The battery optimization seeks to optimally size the battery for a circular orbit. Fixed point iteration between these optimization frameworks yields a flight path and propulsion system that slightly decreases solar capture, but significantly decreases power expended. Fully coupling the trajectory and design optimizations with this level of accuracy is infeasible with current computing resources. These efforts show the benefits of combining design and trajectory optimization to enable the feasibility of SR-HALE flight.