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Ismail M.,Beihang University | Wu Z.,Beihang University | Bakar A.,North Western Polytechnical University of China | Tariq S.,Centers of Excellence in Science and Applied Technologies
Journal of Aerospace Engineering | Year: 2015

In this paper, heavy rain effects on the aerodynamic efficiency of National Advisory Committee for Aeronautics (NACA) 23015 airfoil cruise and landing configurations and NACA 64210 high lift configuration have been studied. For this study, preprocessing software Gridgen has been used for creation of geometry and mesh, and fluent software is used as a solver. Discrete phase modeling (DPM) in a Langrangian frame of reference has been used to simulate the rain particles dispersed in continuous phase using two-phase flow approach. The coupling between the two phases and its impact on both phases has been included. In discrete phase model (DPM), the wall film is modeled by the injected rain particles. In a simulated rain environment all the cruise, landing, and high lift configurations of airfoils showed a significant decrease in lift and increase in drag in heavy rain environment. In this study, it is found that the heavy rain causes premature boundary-layer transition at low angle of attack (AOA) and separation at high AOA. The water film layer formed on the surface of the airfoil is thought to alter the airfoil geometry and increase the mass effectively. In the simulation for NACA 23105 the increase in drag is less contrary to simulations done for NACA 64210 high lift configuration airfoil, for which the aerodynamic efficiency degradation is much higher. The relative differences appeared to be related to the susceptibility of each airfoil to premature boundary-layer transition. Postprocessing software like MATLAB, Tec plot, and Origin are used to see the effects of the heavy rain, and the results obtained are compared with the experimental results. It is strongly believed that this study will be useful for the aviation engineers and scientists to design the airplanes and UAVs capable of flying in severe weather conditions and to train the pilots to control the airplane in heavy rain conditions well. © 2014 American Society of Civil Engineers.

Badshah A.,Robot Vision | Ahsan Q.,Centers of Excellence in Science and Applied Technologies
Proceedings of 2015 12th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2015 | Year: 2015

In today's life Inertial Navigation System (INS) is the main source of finding the attitudes i.e. roll pitch and yaw of a flying object for navigation purposes. Due to the complexity and high cost of this system vision based navigation systems are introduced in this field for exact localization. In both systems rate of change of one function relative to other function is determined. A very sophisticated INS system is required to give an accurate instant values of roll, pitch and yaw of an object at any state during flight, which is much expensive. Vision based techniques are used for localization and aerial navigation. It constitutes straightforward-cheap method to estimate the object location. A single consumer grade camera can replace a typical expensive suit (encoders, IMU etc.). The visual information from successive aerial images is used to estimate the movements of the flying object by applying phase correlation techniques. With the help of these correlation methods the images are registered with each other. The requirements for a successful registration are sufficient illumination in the environment, dominance of static scene over moving objects, enough texture to allow apparent motion to be extracted, and sufficient scene overlap between consecutive frames. In the proposed method modified normalized phase correlation has been used. In particular Gram polynomial basis functions are applied to remove the Gibbs error problem. When flying object changes its direction or AGL (altitude above ground level) the pixel change between two consecutive images is calculated and converted to degrees along and across the flight direction. Which results in the roll and yaw by machine vision. The proposed method is applied on a real time data set and the attitudes results are compared with available INS system's results. As per available information of the INS system, the attitude error growth was typically in the range of 2 to 3 degrees/ hr i.e. 1 sigma. The results achieved by the proposed method are comparable with the available system. © 2015 IEEE.

Wu Z.,Beihang University | Cao Y.,Beihang University | Ismail M.,Centers of Excellence in Science and Applied Technologies
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering | Year: 2015

In this study, the effects of heavy rain on the longitudinal stability and control performance of the DHC-6 Twin Otter aircraft are determined based on data from numerical simulation. A two-way momentum coupled Eulerian-Lagrangian approach for two-phase flows developed in the authors' previous work is adopted to obtain the basic aerodynamic coefficients in rain. Then the aerodynamic derivatives of the aircraft in rain are evaluated on the basis of the narrow-strip theory. Finally, the longitudinal stability and control performance of the aircraft in rain are analyzed. Our research results suggest that heavy rain can cause degradations in both of aerodynamic and flight mechanics performances to an aircraft. In practice, these short-duration rainfall encounters should be avoided as far as possible since rain mainly has significant adverse effects on the short-period mode characteristics. © IMechE 2014.

Khan I.,Centers of Excellence in Science and Applied Technologies | Roth P.M.,University of Graz | Bais A.,University of Regina | Bischof H.,University of Graz
ICET 2013 - 2013 IEEE 9th International Conference on Emerging Technologies | Year: 2013

Semi-supervised learning has recently demonstrated be successful in large scale learning for image classification tasks. Laplacian Support Vector Machines (LapSVM) is one of such approaches applied to this task. However, LapSVM uses a squared hinge loss function for the labeled examples, which is not twice differentiable and may penalize noisy labeled examples too much. Thus, the accuracy decreases when the training data contains outliers or the labeled data is heavily contaminated by noise. We propose to use a continuously differentiable loss function called Huber hinge loss, which gives a milder penalty than the squared hinge loss. Furthermore, we build on the primal formulation of LapSVM and use a preconditioned conjugate gradient method to make the approach more efficient. In this way the training time can be reduced but still a very accurate approximation of the original problem can be obtained. Detailed experimental results validate our proposed strategy for classification problems when the available training data is contaminated with label-noise. © 2013 IEEE.

Mushtaq M.T.,Graz University of Technology | Khan I.,Centers of Excellence in Science and Applied Technologies | Khan M.S.,University of Gujrat | Koudelka O.,Graz University of Technology
Radioengineering | Year: 2015

Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM) classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying) based signals propagating through an AWGN (Additive White Gaussian Noise) channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR) values up to -50 dB.

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