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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

Neural network training results.
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f8-sensors-12-10920: Neural network training results.

Mentions: Data samples acquired in the experiments were used to carry out the model training. After 850,000 training epochs, the sum-squared network error reached less than 0.01 for the sensor arrangement 1 and less than 0.05 for the sensor arrangement 2, as shown in Figure 8.


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

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

Neural network training results.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-12-10920: Neural network training results.
Mentions: Data samples acquired in the experiments were used to carry out the model training. After 850,000 training epochs, the sum-squared network error reached less than 0.01 for the sensor arrangement 1 and less than 0.05 for the sensor arrangement 2, as shown in Figure 8.

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