Aerodynamic Parameter Prediction on a Airfoil with Flap via Artificial Hair Sensors and Feedforward Neural Network

Magar, Kaman S. Thapa; Reich, Gregory W.; Rickey, Matthew R.; Smyers, Brian; Beblo, Richard

doi:10.2514/6.2016-1540

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…A feed-forward neural network to predict the aerodynamic parameters such as angle of attack, freestream velocity, lift coefficient, and moment coefficient using AHS measurement of local velocity was proposed in [19,20] based on simulation study. The main motivation behind the AHS is to sense the local flow information such as local flow velocity, shear stress, pressure, etc; however, local flow phenomena is a complex process and varies with the body immersed on the fluid and flow conditions.…”

Section: Aerodynamic Parameter Predictionmentioning

confidence: 99%

“…At the same time, the longer hair towards the trailing edge ensures the tip of the hair remains outside of the boundary layer. For this study, the leading edge was not considered for the sensor integration because it was observed from the simulation study [19,20] that having a sensor at the leading edge loses this distinctiveness in measurement. The trailing edge was avoided due to the presence of trailing edge flap.…”

Section: Aerodynamic Parameter Predictionmentioning

confidence: 99%

“…The AHS can also be used to detect the boundary layer flow which ultimately can be used to maintain a laminar attached boundary layer flow and reduce the skin friction drag [15]. It has also been shown in simulation [19,20] that the local velocity measurement from the AHS array can be combined with a feed-forward neural network to effectively predict the aerodynamic parameters such as lift and moment coefficient, free-stream velocity, and angle of attack on an airfoil. Real-time force and moments can be obtained from these parameters which have been found to be effective in gust load alleviation on UAS [21].…”

Section: Introductionmentioning

confidence: 99%

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Aerodynamic parameters from distributed heterogeneous CNT hair sensors with a feedforward neural network

Magar¹,

Reich²,

Kondash³

et al. 2016

Bioinspir. Biomim.

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Distributed arrays of artificial hair sensors have bio-like sensing capabilities to obtain spatial and temporal surface flow information which is an important aspect of an effective fly-by-feel system. The spatiotemporal surface flow measurement enables further exploration of additional flow features such as flow stagnation, separation, and reattachment points. Due to their inherent robustness and fault tolerant capability, distributed arrays of hair sensors are well equipped to assess the aerodynamic and flow states in adverse conditions. In this paper, a local flow measurement from an array of artificial hair sensors in a wind tunnel experiment is used with a feedforward artificial neural network to predict aerodynamic parameters such as lift coefficient, moment coefficient, free-stream velocity, and angle of attack on an airfoil. We find the prediction error within 6% and 10% for lift and moment coefficients. The error for free-stream velocity and angle of attack were within 0.12 mph and 0.37 degrees. Knowledge of these parameters are key to finding the real time forces and moments which paves the way for effective control design to increase flight agility, stability, and maneuverability.

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Section: Aerodynamic Parameter Predictionmentioning

confidence: 99%

Section: Aerodynamic Parameter Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aerodynamic parameters from distributed heterogeneous CNT hair sensors with a feedforward neural network

Magar¹,

Reich²,

Kondash³

et al. 2016

Bioinspir. Biomim.

Self Cite

View full text Add to dashboard Cite

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Bio-inspired, Neuromorphic Acoustic Sensing

Lenk,

Ved,

Durstewitz

et al. 2023

Springer Series on Bio- And Neurosystems

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We present an overview of recent developments in the area of acoustic sensing that is inspired by biology and realized by micro-electromechanical systems (MEMS). To support understanding, an overview of the principles of human hearing is presented first. After the review of bio-inspired sensing systems, we continue with an outline of an adaptable acoustic MEMS-based sensor that offers adaptable sensing properties due to a simple, real-time feedback. The transducer itself is based on an active cantilever, which offers the advantage of an integrated deflection sensing based on piezoresistive elements and an integrated actuation using thermomechanical effects. We use a feedback loop, which is realized via a field-programmable gate array or analog circuits, to tune the dynamics of the sensor system. Thereby, the transfer characteristics can be switched between active, linear mode, for which the sensitivity and minimal detectable sound pressure level can be set by the feedback strength (similar to control of the quality factor), and an active nonlinear mode with compressive characteristics. The presented sensing system, which is discussed both from an experimental and theoretical point of view, offers real-time control for adaptation to different environments and application-specific sound detection with either linear or nonlinear characteristics.

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