Aeroelastic Tailoring and Active Aeroelastic Wing Impact on a Lambda Wing Configuration

Alyanak, Edward J.; Pendleton, Ed

doi:10.2514/1.c033040

Cited by 13 publications

(4 citation statements)

References 28 publications

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“… The inclusion of buckling, control reversal, gust, performance, and transonic aerodynamic constraints in future works may further establish the applicability of the designed approach to practical problems. 2017 Alyanak and Pendleton [ 32 ] Aeroelastic tailoring and active aeroelastic wing impact on a lambda wing configuration Two approaches were used for the supersonic lambda wing's aeroelastic tailoring optimization: the first was the Active Aeroelastic Wing (AAW) design approach employing ZAERO in combination with NASTRAN, and the other was the standard NASTRAN approach. Eight variants of a finite element model for studying wing stiffness variation.…”

Section: Latest Developments and State Of The Artmentioning

confidence: 99%

Aeroelastic tailoring for aerospace applications

Najmi,

Khan,

Javaid

et al. 2024

Heliyon

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Section: Latest Developments and State Of The Artmentioning

confidence: 99%

Aeroelastic tailoring for aerospace applications

Najmi,

Khan,

Javaid

et al. 2024

Heliyon

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“…BP neural network consists of input layer, hidden layer and output layer, in which hidden layer can have multiple [9]. Each neuron is connected to the neurons of the previous layer and the latter layer, and each connection has a weight [10]. The training process of BP neural network is to update the weight through back propagation error to minimize the output error.…”

Section: The Initial Prediction Model Of Horizontal Tail Flight Loadmentioning

confidence: 99%

Horizontal Tail Flight Load Prediction Method for Large UAVs based on Machine Learning

Hu,

Pan,

Wang

2024

J. Phys.: Conf. Ser.

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In today’s rapidly developing large UAVs field, flight safety and reliability are essential considerations. Flight load is one of the important factors affecting the load capacity of large UAVs, so the research on load prediction technology can improve flight safety and the reliability of air transportation. This paper first introduces the collection and processing method of flight load data, then uses the machine learning method-BP neural network to predict flight load, establishes the BP neural network model to predict flight load with flight parameters, and optimizes the model according to error analysis. Finally, the accuracy and practicability of the model are verified by analysing the fitting of the training set and the test set, and the error distribution of the true value of shear, bending moment, torque and the absolute percentage of the predicted value, and the model is evaluated.

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“…The AAW is expected to liberate aircraft designers from choosing configurations with lower aspect ratios or adding an aileron-reversal constraint to stiffen the wing, which would make it too heavy. This new technology could take full advantage of slender wing configurations to improve range and control power performance significantly (Alyanak & Pendleton, 2017).…”

Section: Introductionmentioning

confidence: 99%

Active aeroelastic wing application on a forward swept wing configuration

Xue

2019

Engineering Applications of Computational Fluid Mechanics

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Active aeroelastic wing is introduced to benefit the forward swept wing (FSW) aeroelastic performance owing to the FSW's elaborate aeroelastic problem.In this research, a computational aeroelastic (CAE) code was developed to conduct aeroelastic simulations, which presents challenges of data interpolation and flow capturing since torsional deformations instead of bending dominate in FSW deformation and separations on FSW occur earlier due to the boundary layer accumulation. Two simulations are conducted to verify the adaptability and accuracy of CAE method on FSW. CAE simulations are conducted on a straight FSW with control surfaces in two phases. The results from first phase obtained at Mach 0.8 demonstrate that negative deflection of LE and positive deflection of TE can effectively depress the torsion angle and root-bending moment at identical lift. Deflections of LE 0°, TE +20°can reduce LE and TE wingtip displacement by 36.53% and 39.87%, respectively, resulting in 78.97% decline of torsion angle. The second-phase calculations, conducted at Mach 0.9 at different dynamic pressures, illustrate depression of FSW torsion and deformation by LE and TE downward deflection. The LE and TE control effectiveness is always beyond 1 and the control power of the FSW increases with increasing dynamic pressure.

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Aeroelastic Tailoring and Active Aeroelastic Wing Impact on a Lambda Wing Configuration

Cited by 13 publications

References 28 publications

Aeroelastic tailoring for aerospace applications

Aeroelastic tailoring for aerospace applications

Horizontal Tail Flight Load Prediction Method for Large UAVs based on Machine Learning

Active aeroelastic wing application on a forward swept wing configuration

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