Advanced UAV Aerodynamics, Flight Stability and Control

Marques, Pascual

doi:10.1002/9781118928691.ch1

Cited by 23 publications

(24 citation statements)

References 15 publications

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“…Both L2 regularization and dropout [21] techniques were utilized in order to increase the generalization performance of the network and prevent overfitting. This resulted in obtaining a new cost function, which consists of both the cost function defined by Equation (6) and the new scalar regularization value β due to L2 regularization. The final cost function FCF CL is given by Equation 7and the metric used for calculating the accuracy of predictions in load localization is given by Equation (8).…”

Section: Load Localization From Depth Images Using Annmentioning

confidence: 99%

“…Current aircraft maintenance and repair systems used for structural monitoring rely on load monitoring systems while using strain gauges [1,2], optical measurement systems [3][4][5][6][7], and fiber brag grating (FBG) [8][9][10][11] sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Burner et al [3] presented the theoretical foundations of video grammetric model deflection (VMD) measurement technique, which was implemented by National Aeronautics and Space Administration (NASA) for wind tunnel testing [4]. Afterwards, many research on wing deflection measurement and analysis were motivated by the catastrophic failure of the unmanned aerial vehicle (UAV) Helios [5,6]. Lizotte et al [7] proposed estimation of aircraft structural loads based on wing deflection measurements.…”

Section: Introductionmentioning

confidence: 99%

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Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

Bilal

Ünel

Yıldız

et al. 2020

Sensors

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This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem.

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Section: Load Localization From Depth Images Using Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

Bilal

Ünel

Yıldız

et al. 2020

Sensors

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“…Results showed that the maximum stress occurs at distance of 0-15% from the edge of wing by using software program (ANSYS11). it used pressure distribution on the wing to know the distribution of tension and displacement of the structure of the wing (Marqués, 2017, Tischler, 2018.…”

Section: Introductionmentioning

confidence: 99%

Pseudorandom Noise Impulsive Response Analysis and Aerodynamic Forces of Unmanned Aerial Vehicle Structure

Salman¹,

Mohsen²,

Abttan³

2019

ujme

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This paper concentrated about the effect of both the pseudorandom or random vibration (wind waves) and aerodynamic forces on the wing of unmanned aerial vehicle, which brought the attention of specialists in this field during last years, the performance of wing is improved on a definitive solution for the vibration problems which cause failure in the wings of UAV. The distribution of stresses and distortions with aerodynamic loads is studied. Factors such as tension, pressure and shear stress showed on wing of UAVs due to vibration which caused the structure of wing to break down and then failure. The experimental study was carried out by using wing made of composite material (foam and cover by lamination plate), where airfoil type (NACA Clark y) installed inside wind tunnel of low velocity. It is found that the vibration acceleration at constant wind velocity with variation of attack angle of the wing, it is obtained the relationship between the acceleration and the frequency using the LABVEIW program which analyzed and identified the distribution of forces on the wing. The stress concentration areas is created and found under failure occurs, the aerodynamic force, torsion torque and magnitude of deformation is calculated. It is concluded that the close areas from the root wing (fixed end) is most likely to collapse or break.

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“…Wind tunnel test is high-cost and time-consuming, most of all, the results are not accurate in the flight condition of low Reynolds number. And CFD software analysis usually has heavy work on the 3D model meshing, and the gap effect as well as the frictional drag is often neglected [ 4 , 5 ]. DATCOM, introduced by the US Air Force, is another choice for the parameter calculation.…”

Section: Introductionmentioning

confidence: 99%

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs

Jieliang

Liang

2018

Sensors

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The establishment of the Aircraft Dynamic Model (ADM) constitutes the prerequisite for the design of the navigation and control system, but the aerodynamic parameters in the model could not be readily obtained especially for small-scaled fixed-wing UAVs. In this paper, the procedure of computing the aerodynamic parameters is developed. All the longitudinal and lateral aerodynamic derivatives are firstly calculated through semi-empirical method based on the aerodynamics, rather than the wind tunnel tests or fluid dynamics software analysis. Secondly, the residuals of each derivative are proposed to be identified or estimated further via Extended Kalman Filter (EKF), with the observations of the attitude and velocity from the airborne integrated navigation system. Meanwhile, the observability of the targeted parameters is analyzed and strengthened through multiple maneuvers. Based on a small-scaled fixed-wing aircraft driven by propeller, the airborne sensors are chosen and the model of the actuators are constructed. Then, real flight tests are implemented to verify the calculation and identification process. Test results tell the rationality of the semi-empirical method and show the improvement of accuracy of ADM after the compensation of the parameters.

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Advanced UAV Aerodynamics, Flight Stability and Control

Cited by 23 publications

References 15 publications

Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

Pseudorandom Noise Impulsive Response Analysis and Aerodynamic Forces of Unmanned Aerial Vehicle Structure

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs

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