Identification of aircraft flight dynamic modes has been implemented by adopting highly nonlinear flight test data. This paper presents a new algorithm for identification of the flight dynamic modes based on Hilbert–Huang transform (HHT) due to its superior potential capabilities in nonlinear and nonstationary signal analysis. Empirical mode decomposition and ensemble empirical mode decomposition (EEMD) are the two common methods that apply the HHT transform for decomposition of the complex signals into instantaneous mode frequencies; however, experimentally, the EMD faces the problem of “mode mixing,” and EEMD faces with the signal precise reconstruction, which leads to imprecise results in the estimation of flight dynamic modes. In order to overcome (handle) this deficiency, an improved EEMD (IEEMD) algorithm for processing of the complex signals that originate from flight data record was introduced. This algorithm disturbing the original signal using white Gaussian noise, IEEMD, is capable of making a precise reconstruction of the original signal. The second improvement is that IEEMD performs signal decomposition with fewer number of iterations and less complexity order rather than EEMD. This algorithm has been applied to aircraft spin maneuvers flight test data. The results show that implication of IEEMD algorithm on the test data obtained more precise signal extractions with fewer iterations in comparison to EEMD method. The signal is reconstructed by summing the flight modes with more accuracy respect to the EEMD. The IEEMD requires a smaller ensemble size, which results in saving of a significant computational cost.
A transition prediction method for flow over airfoils based on high-order dynamic mode decomposition
