The objective of the proposed effort is to develop a systematic approach for building reduced-order models (nonlinear coupled finite-degree-of-freedom equations) capable of reliably modeling and predicting limit cycle oscillations (LCOs) and using these models in LCO management procedures. The models will be developed by combining (a) physical observations from ground vibration, wind tunnel, and flight test data, (b) nonlinear physical equations governing the relevant rigid-body motions and the elastic deformations of the wing capable of producing the observed physical phenomena, and (c) system identification of linear and nonlinear parameters using a combination of higher-order spectral analysis of the test data and approximate solutions of the nonlinear coupled finite-degree-of-freedom equations. The developed reduced-order models will be validated using other test data. The models will be used to investigate the root causes of LCO and predict safe operational envelopes. The effectiveness of the control surfaces in controlling the aerodynamic loads and structural response in the transonic regime will be quantified and modeled. The models will also be used to design passive and active strategies to manage LCO. In the passive case, the normal form of the instability will be developed to ascertain the contribution of the different types of nonlinearities to LCO and then design nonlinear elements that can be embedded in the wing structure to transform subcritical instabilities to supercritical ones and reduce LCO amplitudes. For the active case, an adaptive nonlinear feedback strategy based on autoparametric resonance (two-to-one-internal resonance) will be designed to mitigate LCO. Adaptivity will be introduced in the control law to deal with variations in the frequencies of the interacting modes induced by shifts in their vibration amplitudes, varying external conditions, or vibrations due to unpredictable inputs with unknown frequencies (e.g., gust).
Keywords: Limit Cycle Oscillations, Nonlinear Aeroelasticity, Wing-Store Configuration, Reduced-Order Models, Nonlinear System Identification, Higher-Order Spectral Methods, Hopf Bifurc
