<p> Modern unmanned aerial vehicles (UAV) are made of lightweight structures, owing to the demand for longer ranges and heavier payloads. These lightweight aircraft are more susceptible to vibrations caused by atmospheric turbulence transmitted to the fuselage from the wings. These vibrations, which can cause damage to the payload or on board avionics present a serious problem, since air turbulence is expected to increase over the next few decades, due to climate change. </p><p> The objective of this thesis is to predict the vibration of an aircraft fuselage by establishing a relationship between wing and fuselage vibration. A combination of ANSYS<sup>®</sup> and MATLAB<sup>®</sup> modeling are used to simulate aircraft vibrations. First, the displacement of a lumped mass aircraft model to step and sinusoidal forces acting on the wings are compared to displacements calculated using modal superposition equations. Next, a state space representation of this system is found using system identification techniques, which uses wing displacement as input, and provides fuselage displacement as output. This state space model is compared to a derived state space model for validation. Finally, a three dimensional aircraft with distributed displacement sensors on its wings is modeled. A state space representation is established using the wing displacement output from the sensors as its input and the motion and rotation of the fuselage along the X, Y and Z axes as the output. </p><p> It is seen that the displacement results of the lumped mass system match with those calculated using modal superposition equations. The state space model can also accurately predict the fuselage vibration of the lumped mass system, when provided with wing displacement as input. More importantly, results have shown that the distributed vibration sensors on the three dimensional plane model are able to measure the wing displacements. Using the output from these distributed sensors, the motion and rotation of the fuselage about all three axes can be effectively predicted.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1594387 |
| Date | 01 August 2015 |
| Creators | Thomas, Rohan J. |
| Publisher | The University of North Dakota |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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