基于云自适应粒子群算法的高压转子复杂接触有限元模型参数修正

张子阳; 谢寿生; 王海涛; 龙门; 苗卓广

基于云自适应粒子群算法的高压转子复杂接触有限元模型参数修正

1.
空军工程大学工程学院, 西安 710038
2.
空军工程大学电讯工程学院, 西安 710077

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- 被引次数: 0
出版历程
- 收稿日期: 2011-01-05
- 修回日期: 2011-05-08
- 刊出日期: 2011-12-28

Contacting optimization in high pressure spool finite element parameters updating based on adaptive cloud PSO model

1.
The Engineering Institute, Air Force Engineering University, Xi'an 710038, China
2.
Telecommunication Engineering Institute, Air Force Engineering University, Xi'an 710077, China

摘要

摘要: 为建立更加准确的航空发动机高压转子的有限元模型,提出一种修正有限元模型描述航空发动机复杂接触的方法.将修正问题转化为求解定义在时域的误差函数的极小值,运用云自适应方法动态调整粒子群算法的惯性权重,使得算法在接近较优解时,惯性因子分布在云低端,有利于收敛得到更优解;当问题解较差时,其惯性因子分布在云顶端,有利于跳出局部极小点,扩大搜索范围.以仿真算例和实际航空发动机高压转子模型为例,通过与相关算法的修正结果比较,证明该算法是可行且有效的.
- 高压转子 /
- 接触模型 /
- 修正有限元 /
- 云自适应分布模型 /
- 粒子群优化
Abstract: A method to describe the complex contacting surfaces was propounded to update finite element(FE) model of high pressure spool in aero-engine.It was posed as the minimization of an error function defined in time domain.The minimization was performed by using a novel particle swarm optimization method based on adaptive cloud distributing model,which constituted the main contribution in this work.In this algorithm,when the solution was close to the minimum,the weights of particle distribution were at the top of the cloud model which can find the nearest minima accurately,and when this algorithm tended to be unstable,the weights were at the bottom of the cloud model,which lowered the chance to relapse into local optimization in the process of solution convergence.The method has been tested through a simulated function model and FE model of high pressure spool.The performance of the proposed adaptive algorithm is more effective than those of other related algorithm with which it is compared.
- high pressure spool /
- contacting model /
- finite element model updating /
- adaptive cloud distributing model /
- particle swarm optimization(PSO)

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参考文献(18)

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