Unlock instant, AI-driven research and patent intelligence for your innovation.

New particle swarm new algorithm for recognizing nonlinear hysteretic kinetic model parameter

A dynamic model and parameter identification technology, which is applied in computing, electrical digital data processing, special data processing applications, etc., can solve problems such as falling into local extremum and failing to obtain the optimal solution, and achieve rapid identification, improved convergence performance, The effect of accurate parameters

Inactive Publication Date: 2017-06-23
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF1 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as a random search algorithm, the standard particle swarm optimization algorithm is prone to fall into local extremum, premature convergence occurs, and the real optimal solution cannot be obtained.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • New particle swarm new algorithm for recognizing nonlinear hysteretic kinetic model parameter
  • New particle swarm new algorithm for recognizing nonlinear hysteretic kinetic model parameter
  • New particle swarm new algorithm for recognizing nonlinear hysteretic kinetic model parameter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0050] In order to reflect the actual effect of the present invention, the parameter identification method of the present invention is used to identify two nonlinear hysteretic dynamic models, namely the Leishman-Beddoes dynamic stall model and the parameters of the hysteretic displacement field model of the viscoelastic damper. These two models are very important for analyzing the dynamics of helicopter rotor aero bombs and improving the stability of rotor aero bombs. It should be noted that the implementation examples here are only used to explain the present invention, not to limit the present invention.

example 1

[0051] Example 1. When the airfoil angle of attack changes dynamically, due to the effect of airflow separation and dynamic stall, the angle of attack-normal force coefficient curve of the airfoil profile shows hysteresis. The Leishman-Beddoes dynamic stall model can accurately calculate the airfoil aerodynamic changes with the angle of attack, but this model has more model parameters and needs to be identified by experimental data.

[0052] Using the new particle swarm algorithm proposed by the present invention, the parameters of the Leishman-Beddoes dynamic stall model of the airfoil are identified. The number of particles arranged in the particle swarm algorithm is 10, the number of iteration steps is 100, and the number of repetitions is 50.

[0053] The NACA0012 airfoil has a chord length of 0.1 meters, an incoming Mach number of 0.38, and an angle of attack change of α=10.3°+8.1°sin (0.075S). After 50 repeated identifications, the airfoil angle of attack-normal force coeffi...

example 2

[0055] Example 2. A viscoelastic damper made of metal and rubber can increase the rigidity and damping of the structure, play a role in energy dissipation and vibration reduction, and is widely used in various fields such as aerospace, high-rise buildings, and mechanical engineering. Under the action of alternating load, the strain and stress of the viscoelastic damper will show nonlinear hysteresis characteristics.

[0056] The hysteretic displacement field model can accurately simulate the stress-strain hysteresis loop of the viscoelastic damper, and its model parameters need to be identified using experimental data.

[0057] Using the new particle swarm algorithm proposed by the present invention, the parameters of the viscoelastic damper hysteretic displacement field model are identified. The number of particles arranged in the particle swarm algorithm is 10, the number of iteration steps is 100, and the number of repetitions is 50. The frequency of the alternating load on the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a new particle swarm algorithm for recognizing a nonlinear hysteretic kinetic model parameter, and belongs to the field of recognition of nonlinear hysteretic kinetic model parameters and swarm intelligent algorithms. According to the technical scheme, aiming at characteristics of a nonlinear hysteretic kinetic model, and based on a standard particle swarm algorithm, the improved particle swarm algorithm for recognizing the nonlinear hysteretic kinetic model parameter is built by defining a specific fitness function. According to the new particle swarm algorithm for recognizing the nonlinear hysteretic kinetic model parameter, the convergence performance of the standard particle swarm algorithm is improved, the capacity of the algorithm in global optimization in an earlier-stage iterative process and in local optimization in a later iterative process can be improved, the situations that the optimization recognition process is caught in a local extremum and premature convergence occurs are avoided, and the kinetic model parameter which has nonlinear hysteretic characteristics can be accurately and quickly identified.

Description

Technical field [0001] The invention relates to a new particle swarm algorithm for parameter identification of a nonlinear hysteresis dynamic model, belonging to the field of nonlinear hysteresis system parameter identification and swarm intelligence algorithms. Background technique [0002] The hysteresis characteristic is a kind of nonlinear dynamic characteristic, which widely exists in materials such as rubber, piezoelectric ceramics and magnetorheological fluid. When performing nonlinear dynamic analysis on such materials, in order to establish an accurate dynamic model, a nonlinear hysteretic dynamic model is required. The nonlinear hysteresis dynamic model can accurately simulate the nonlinear hysteresis characteristics of the structure, but it has more model parameters and needs to be identified by optimization algorithms. Due to the large number of parameters in the nonlinear hysteretic dynamics model, there is a coupling between each other, which greatly increases the ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 张俊豪夏品奇
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS