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Title: Efficient analysis of nonlinear aeroelastic systems under uncertainty
Author: Hayes, Richard
ISNI:       0000 0004 6061 141X
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2016
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In aircraft design, considerations to aeroelastic interactions are necessary to define permitted operating conditions which are safe. Limit cycle oscillations (LCO) are an example of unwanted and potentially dangerous wing behaviour. These are constant amplitude structural vibrations, driven by the interaction between a wing and the surrounding airflow. The present work is concerned with the numerical analysis of LCOs. More specifically, addressing the lack of comprehensive stochastic analyses of these highly nonlinear phenomena by utilising techniques which improve efficiency and support high complexity. This will allow simulation to be used more extensively in the aircraft design process which will enable safety factors to be rationalised thus improving aircraft performance. The cost of LCO simulation is reduced by implementing a High-Dimensional Harmonic Balance-based (HDHB) formulation. This method showed excellent agreement with the corresponding time-marching models and efficiency gains surpassed one order of magnitude. An objective of this work is to implement HDHB to problems with nonlinearities in both structural and flow field characteristics. Polynomial Chaos Expansions (PCE) are employed for the propagation of parametric uncertainties, offering large efficiency gains in comparison to Monte Carlo methods. Bifurcations uncovered in the stochastic analysis were handled well by the PCE by partitioning the parameter space along the discontinuity. Stochastic model updating, also enabled by the efficiency of the HDHB method, is explored to tackle the difficulties in establishing sources of nonlinearities within aeroelastic problems. A Bayesian inference technique is used to perform the parameter estimations of elusory structural characteristics for the purpose of model calibration. Parameter values were able to be identified with high accuracy using sparse observational data. This work has shown that the HDHB method provides an attractive alternative to timemarching methods. In addition, when combined with PCE, the stochastic analysis of highly complex LCOs is realisable.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available