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From albatross to long range UAV flight by dynamic soaring

Bonnin, Vincent

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Authors

Vincent Bonnin



Abstract

In the domain of UAVs, endurance and range are key utility factors. However, small-sized UAVs are faced with serious limitations regarding energy storage options. A way to address this challenge is to seek for energy from the surrounding environment. One flight technique, called dynamic soaring, has been perfected by large seabirds like the albatross, which enables them to wander effortlessly in southern oceans. This thesis investigates the feasibility to find inspiration from the biological world in order to address the issue of limited endurance.
First of all, an extensive literature background sums-up a range of technical aspects that can be learnt out of the flight of albatrosses. It reviews their morphology, flight performance and sensitivity to wind strength, their flight characteristics and energy expenditure management. Then, a methodology to simulate dynamic soaring flight is built-up by focusing first on adequate models for the vehicle and for the environment. It details the way those models are described quantitatively and qualitatively. As for the vehicle, a point mass model is chosen and applied to fixed-wing gliders of several scales, as well as to an albatross of generic dimensions. The environment is first modelled by classical boundary layer theory on a rather flat surface and then refined by taking into account specificity about the ocean boundary layer, such as varying roughness length and surface waves. Equations of motion are detailed for both points of views, earth-relative and air relative. This yields two different sets of equations of motion, eventually representing equivalent physics. An optimization problem is then set in order to determine, for the vehicle, how to extract energy from its environment. Variations in objective function and in constraints are described before presenting the numerical integration scheme which converts the optimization problem into that of finite-dimension. The solving tools and their specificity are presented, followed by a validation of the overall
methodology with a particular study case from the literature. Basic principles of dynamic soaring flight are explicated by using a specific closed-loop study case. Energy-harvesting mechanisms are disclosed locally and next integrated over the whole flight path. A further illustration of dynamic soaring is provided by relaxing some periodicity constraints and opening the trajectory. The specificity of the ocean boundary layer environment is finally implemented and a refined energy-harvesting strategy is presented. Air relative equations of motion are dimensionless so as to highlight specific dynamic soaring behaviours, in the case of a simplified linear wind profile and eventually by finding an appropriate non-dimensionalization for a logarithmic wind profile. Conditions of similarities between dimensionless solutions are described and some basic dynamic soaring characteristics are outlined. Finally, various dynamic soaring performance study case are computed. Optimized trajectories are implemented for the selected vehicles and compared on a required wind strength basis. The sensitivity of the required wind strength to the net flight heading as well as to the ground clearance and to the surface roughness length is determined by drawing performance charts. In order to enlarge the scope of favourable dynamic soaring conditions, thrust-augmented trajectories are considered. The range improvements offered by dynamic soaring are compared to a straight line case, for different wind strength and different net flight headings.

Citation

Bonnin, V. From albatross to long range UAV flight by dynamic soaring. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/909425

Thesis Type Thesis
Publicly Available Date Mar 28, 2024
Keywords dynamic soaring, UAV, albatross, long range, optimized trajectory, energy-harvesting
Public URL https://uwe-repository.worktribe.com/output/909425
Award Date Jul 30, 2016

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