Seven-hole pressure probe calibration method utilizing look-up error tables

Wenger, Christian W.; Devenport, William J.

doi:10.2514/3.14226

Cited by 9 publications

(7 citation statements)

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“…The V a , α, and β values are interpolated from large 3D tables with either full or subsets of the training data. This method has been used before in FADS and conventional probe systems [23].…”

Section: Regression Analysismentioning

confidence: 99%

Flush Air Data Sensing for Soaring-Capable UAVs

Quindlen

Langelaan

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Dynamic soaring extracts energy from naturally occurring wind gradients that can be used to extend aircraft endurance, particularly in small UAVs. Autonomous thermal soaring has already been validated in flight tests with small UAVs, but this level of demonstration has not been performed for dynamic soaring UAVs, partly due to the precise wind measurements required. This paper explores wind vector sensing using flush pressure ports located on the nose of small sailplanes. Single Hidden Layer neural networks are used to generate regression models to calculate the airspeed, angle of attack, and angle of sideslip and are trained with pressure measurements collected during wind tunnel tests. These models are then applied to pressure measurements taken during flight tests of the aircraft and sensing system to judge the viability of the method.

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Section: Regression Analysismentioning

confidence: 99%

Flush Air Data Sensing for Soaring-Capable UAVs

Quindlen

Langelaan

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…The signal conditioning output was fed into data acquisition system programmed by LabVIEW and the signals were monitored using an oscilloscope. The probe was then calibrated in situ, using the methods presented by Wenger and Devenport [59]…”

Section: Seven-hole Pressure Probementioning

confidence: 99%

Control of Leading Edge Vortices Using Apex Flap over Non-Slender Delta Wing

Rehman

2013

AMM

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This manuscript presents the vortex flow structure over non-slender delta wing with leading edge sweep angle, Λ=45°. A comprehensive investigation has been conducted in wind tunnel at Reynolds number ranging from, Re = 247,000 - 445,000. Seven-hole pressure probe measurements for axial vorticity, axial velocity, vortex trajectory and pressure variations are presented at various chordwise stations and angles of incidences. It was demonstrated that weak leading edge vortices are generated very close to the wing surface with strong shear layer which move upward and outboard with apex flap deflection. Reattachment line move towards wing root chord with the increase in angle of attack. Passive apex flap has been used to control the leading edge vortices and to delay the vortex breakdown. It is recognized that vortex breakdown was delayed by 8% by downward apex flap deflection.

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“…To further understand the sensitivity to sensor failure, a sensitivity index for speed, angle-of-attack and sideslip was developed, Equation (5). Values for the sensitivity indices were calculated at (u, a, (3) = (37ms -1 , 15°, 0°) for each tapping, Fig.…”

Section: Effect Of Tapping Failuresmentioning

confidence: 99%

A neural network approach to the calibration of a flush air data system

Crowther¹,

Lamont

2001

Aeronaut. j.

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A flush air data system uses surface pressure measurements to obtain speed and aerodynamic orientation of a flight vehicle. This paper investigates the use of a neural network to calibrate the air data system on a low-observable aircraft forebody with 22 pressure tappings. Wind tunnel data were obtained for between 0 and 25° angle of attack, 0 to 10° sideslip for speeds of 32, 37 and 45ms-1. Experimental data were used to train multilayer perceptron neural networks. A calibration accuracy of 0·322ms-1, 0·811° and 0·552° for speed, alpha and beta respectively was achieved just using the first five tappings on the nose cone. Increasing the number of tappings used as inputs to the neural network reduces the calibration error. A neural network with 22 inputs gives a best accuracy of 0·095ms-1, 0·15° and 0·085° for speed, α and β respectively. Trained networks show poor robustness to single sensor failures. Robustness is improved by preprocessing input data with an autoassociative network or by introducing sensor redundancy.

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Seven-hole pressure probe calibration method utilizing look-up error tables

Cited by 9 publications

References 0 publications

Flush Air Data Sensing for Soaring-Capable UAVs

Flush Air Data Sensing for Soaring-Capable UAVs

Control of Leading Edge Vortices Using Apex Flap over Non-Slender Delta Wing

A neural network approach to the calibration of a flush air data system

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