Aerodynamic Parameter Identification and Uncertainty Quantification for Small Unmanned Aircraft

2017 ◽  
Vol 40 (3) ◽  
pp. 680-691 ◽  
Author(s):  
Lawrence E. Hale ◽  
Mayuresh Patil ◽  
Christopher J Roy
Keyword(s):  
Parameter Identification ◽  
Uncertainty Quantification ◽  
Unmanned Aircraft ◽  
Aerodynamic Parameter

scholarly journals Angle of Attack Sensor for Small Fixed-Wing Unmanned Aerial Vehicles

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 19
Author(s):  
Wanngoen ◽  
Saetunand ◽  
Saengphet ◽  
Tantrairatn
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Flow Direction ◽  
Air Flow ◽  
Angle Of Attack ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Aerodynamic Parameter ◽  
Aerial Vehicles ◽  
Flow Changes ◽  
Good Agreement

The angle of attack (AOA) is an important parameter for estimating aerodynamic parameter the performance and stability of aircraft. Currently, AOA sensors are used in general aircraft. However, there is no a reasonable-price AOA sensor that is compatible to a small fixed-wing unmanned aerial vehicles (UAVs). This research aims to designs and constructs angle of attract (AOA) sensor for small fixed-wing unmanned aircraft. Mechanism Design, which is similar to aerodynamic wheatear vane, can operate in airspeed 10–30 m/s. The direction of airfoil aligns with the air flow direction. When the AOA of the UAV changes, the air flow changes the direction, resulting in the change of airfoil direction. The high-resolution rotary encoder, that was used to measure the angle of the airfoil, was installed with the fin airfoil. For experiment, the accuracy of the AOA sensor was validated by comparing the angles obtained from the encoder with the standard rotary table in static and wind tunnel. Finally, the AOA sensor, which was attached on aircraft, was verified and recorded in flight test. As the results of the measurement, the airfoil angles detected by the encoder were in good agreement with the standard angles.

scholarly journals Flutter Derivatives Identification and Uncertainty Quantification for Bridge Decks Based on the Artificial Bee Colony Algorithm and Bootstrap Technique

2021 ◽  
Vol 11 (23) ◽  
pp. 11376
Author(s):  
Zhouquan Feng ◽  
Yang Lin
Keyword(s):  
Parameter Identification ◽  
Uncertainty Quantification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Bridge Decks ◽  
Weighted Least Squares ◽  
Bee Colony ◽  
Bootstrap Technique ◽  
Sectional Model ◽  
Flutter Derivatives

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.

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