Nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter

A reduced-order model, aerodynamic technology, applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as poor nonlinear aerodynamic prediction effect, etc., to achieve improved calculation efficiency, high identification accuracy, and strong adaptability Effect

Pending Publication Date: 2018-06-19
BEIHANG UNIV
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  • Description
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Problems solved by technology

[0005] 1. Traditional signals are less effective in p

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  • Nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter
  • Nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter
  • Nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter

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Embodiment Construction

[0023] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0024] The present invention is applicable to the large angle of attack nonlinear aerodynamic reduction model of stall flutter, such as figure 1 shown, obtained through the following steps:

[0025] Step 1: Design the input signal.

[0026] The input signal is designed to predict the stall flutter of the wing. When the wing has a large angle of attack limit cycle vibration, the vibration form of each cycle is sinusoidal. The design method of the present invention is, for a wing model, according to the amplitude, frequency and vibration law of its limit cycle vibration, design left and right symmetrical multi-level sinusoidal signals, these sinusoids include different amplitude and frequency information, and The required amplitude and frequency information are covered; the frequency is generally low-frequency vibration, so the signal frequency range only nee...

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Abstract

The invention discloses a nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter. Firstly, a sine multi-level signal is designed as a nonlinear aerodynamic force recognition signal, and the amplitude of the designed signal needs to meet a large amplitude motion requirement. Secondly, according to a circular neural network model based on deep learning, nonlinear aerodynamic force recognition is carried out, and a CFD method is adopted for calculating a training output signal; according to the circular neural network model based on deep learning, a nonlinear aerodynamic force model of a dynamic time domain when wings vibrate with large amplitude is created, system response under a testing signal is calculated through utilization of CFD, and comparison with a recognition result of the network model is carried out to verify the performance of the model. The design cost of an aircraft is saved, the design efficiency of stall flutter is improved, thenonlinear aerodynamic force order reduction model is developed, and therefore stall flutter can be fast predicted.

Description

technical field [0001] The invention belongs to the field of aircraft design and system identification, and is a non-linear reduced-order model developed for rapidly predicting aircraft stall flutter characteristics and improving calculation efficiency. Background technique [0002] Stall flutter is a self-excited vibration caused by nonlinear aerodynamic force and elastic structure coupling caused by airflow separation when the aircraft wing or rudder surface is at a large angle of attack. Stall flutter exhibits strong nonlinear characteristics. When an aircraft flies at a high angle of attack (for example, a fighter or missile has high maneuverability and agility and needs to fly within a range of a large angle of attack) or encounters a gust of wind, it may cause the lifting surface to stall. When the angle of attack reaches a certain critical value, it is easy to This will lead to aerodynamic stall, which may lead to stall flutter, which will affect the structural safety...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/04G06N3/08
CPCG06N3/084G06F30/15G06F30/20G06N3/045Y02T90/00
Inventor 戴玉婷向正平朱斯岩
Owner BEIHANG UNIV
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