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Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

Que R, Zhu R - Sensors (Basel) (2012)

Bottom Line: Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft.For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed.A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. katykob@163.com

ABSTRACT
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

No MeSH data available.


Related in: MedlinePlus

The collocation of hot film flow sensor array on a MAV.
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f1-sensors-12-10920: The collocation of hot film flow sensor array on a MAV.

Mentions: In this paper, a novel and practical approach, by which the flow fields around the wing are measured and used to deduce multiple aerodynamic parameters of the aircraft, is proposed and validated through wind tunnel experiments. We propose the use of a micro hot-film flow sensor array and a back-propagation (BP) neural network to deduce three aerodynamic parameters: air speed, angle of attack and angle of sideslip. The sensors are tailor-developed on a flexible substrate and home fabricated. The hot film sensors are collocated on specific positions of the wing surface of a micro air vehicle, as illustrated in Figure 1. Two different arrangements: (1) hot film sensors 1, 2, 3, 4 and the Pitot tube; (2) hot film sensors I, II, III, IV and the Pitot tube, are proposed, tested and compared. The readings of the sensors are acquired and converted into digital signals as the inputs of the signal processor, whose outputs deduce the three aerodynamic parameters using a neural network-based data fusion technique. This methodology takes the merits of tiny sensing system, smooth and flexible traits, efficient data processing, and is thus especially suitable for applications on small UAVs and MAVs.


Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

Que R, Zhu R - Sensors (Basel) (2012)

The collocation of hot film flow sensor array on a MAV.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472866&req=5

f1-sensors-12-10920: The collocation of hot film flow sensor array on a MAV.
Mentions: In this paper, a novel and practical approach, by which the flow fields around the wing are measured and used to deduce multiple aerodynamic parameters of the aircraft, is proposed and validated through wind tunnel experiments. We propose the use of a micro hot-film flow sensor array and a back-propagation (BP) neural network to deduce three aerodynamic parameters: air speed, angle of attack and angle of sideslip. The sensors are tailor-developed on a flexible substrate and home fabricated. The hot film sensors are collocated on specific positions of the wing surface of a micro air vehicle, as illustrated in Figure 1. Two different arrangements: (1) hot film sensors 1, 2, 3, 4 and the Pitot tube; (2) hot film sensors I, II, III, IV and the Pitot tube, are proposed, tested and compared. The readings of the sensors are acquired and converted into digital signals as the inputs of the signal processor, whose outputs deduce the three aerodynamic parameters using a neural network-based data fusion technique. This methodology takes the merits of tiny sensing system, smooth and flexible traits, efficient data processing, and is thus especially suitable for applications on small UAVs and MAVs.

Bottom Line: Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft.For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed.A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. katykob@163.com

ABSTRACT
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

No MeSH data available.


Related in: MedlinePlus