Numerical Simulation of Maneuvering Aircraft by Aerodynamic, Flight Mechanics and Structural Mechanics Coupling
Selected results of the DLR Project SikMa-"Simulation of Complex Maneuvers" are presented. The objective of the SikMa Project is to develop and validate a numerical tool to simulate the unsteady aerodynamics of a free flying aeroelastic combat aircraft, by use of coupled aerodynamic, flight mechanics and aeroelastic computations. To achieve this objective, the unstructured, time-accurate flow-solver TAU is coupled with a computational module solving the flight mechanics equations of motion and a structural mechanics code determining the structural deformations. The numerical results are validated by experimental data. For this purpose several specific wind tunnel experiments with different wind tunnel models are carried out.
Assessment of Sting Effect on X-31 Aircraft Model Using CFD
The article describes the computational evaluation of the effect a belly mounted sting on the aerodynamics characteristics of an X-31 wind tunnel model. The investigation includes results of steady and time-dependent CFD analysis of the X-31 model with and without sting. The values of the steady lift and pitching moment coefficients as well as the pitching moment of model undergoing a manuever are compared to each other and to the wind tunnel data. The results of this analysis show the detrimental effect of sting on stability of the vortical structure above the wing as well as a substantial increase of noise in pitching moment data.
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.