基于新型粒子群算法的结构动力学热振模型修正

doi:10.7527/S1000-6893.2022.26559

Abstract

Abstract:

A dynamics model updating method for structures in high temperature environments based on a novel Particle Swarm Optimization （PSO） algorithm is proposed and successfully applied to the updating of the Finite Element （FE） model of a typical complex multi-component structure. The LOPSO algorithm was first designed by combining the Lévy flight strategy and the orthogonal learning method to overcome premature convergence while the PSO solves nonlinear optimization problems such as parameter identification and model updating. Then the proposed method was used to update the equivalent stiffness and damping parameters of shafting bearings. It is proved that the new algorithm has higher accuracy. Finally， we updated the FE model of a typical complex multi-component structure in high temperature environment based on the vibration experiment by the LOPSO method. After updating， the errors of the main modal frequencies in different temperature environments were reduced to less than 7%. The accuracy of the FE model was significantly improved， verifying the effectiveness of the proposed method and its applicability to engineering problems.

Key words: particle swarm optimization, Lévy flight, orthogonal learning, high temperature, finite element (FE), model updati

CLC Number:

V219

Zhenyu WANG, Jizhen WANG, Jingyi YANG, Pengyuan HE, Chenghao PAN, Lingbo ZHOU, Cheng HE, Huan HE. Dynamics model updating of structures at high temperature based on novel particle swarm optimization algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(7): 226559-226559.

Figures/Tables 16

Fig. 1

Fig. 2

Table 1

Fig. 3

Table 2

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 3

Uncertain material parameters

弹性模量参数	连接部位
$E R 1$	飞翼与其支座连接部分
$E R 2$	飞翼支座下部
$E W 1$	舵面底部与其支座连接部分
$E W 2$	舵面支座
$E M 1$	头部与二舱连接处
$E M 2$	二舱与三舱连接处
$E M 3$	三舱与四舱连接处
$E M 4$	四舱与尾舱连接处
$E F 1$	整流罩主体
$E F 2$	整流罩连接块

Table 3

Fig. 11

Table 4

Table 5

Comparison of parameters before and after modal updating

温度/℃	$E R 1$ /MPa	$E R 2$ /MPa	$E W 1$ /MPa	$E W 2$ /MPa	$E F 1$ /MPa
14	8 808.15	27 217.26	54 322.74	27 578.87	132 950.17
300	9 003.25	25 600.99	52 051.43	27 808.52	151 922.81
500	9 123.10	24 564.37	49 511.16	28 010.04	165 139.50

Table 5

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参数	种群数	迭代步数	总计算数	总时间/s
PSOLF	200	50	10 000	32 247
LOPSO	10	50	10 000	32 823

参数	实际	修正值		误差/%
参数	实际	PSOLF方法	LOPSO方法	PSOLF方法	LOPSO方法
k₁/（N·m^-1）	191 900.00	180 100.12	193 360.63	6.15	0.76
k₂/（N·m^-1）	130 570.00	133 679.93	130 717.01	2.38	0.11
k₃/（N·m^-1）	243 000.00	244 200.33	242 930.51	0.49	0.03
c₁/（N·S·mm^-1）	55.00	52.37	54.84	4.78	0.29
c₂/（N·S·mm^-1）	25.00	26.80	24.94	7.20	0.24
c₃/（N·S·mm^-1）	12.00	12.43	11.97	3.58	0.25

温度 /℃	模态阶数	f_t /Hz			e_f_，b/%	e_f_，a/%
温度 /℃	模态阶数	实验值	修正前	修正后	e_f_，b/%	e_f_，a/%
14	1	58.26	63.08	56.24	8.27	3.47
	2	48.38	50.25	48.45	3.87	0.14
	3	132.37	157.64	134.35	19.09	1.50
	4	129.10	148.77	131.47	15.24	1.84
	5	259.52	284.44	247.50	9.60	4.63
	6	65.92	89.12	63.70	35.19	3.37
	7	179.54	217.06	180.55	20.90	0.56
	8	384.50	449.74	382.9	16.97	0.42
300	1	54.86	59.95	54.12	9.28	1.35
	2	47.19	48.22	45.29	2.18	4.03
	3	128.02	136.13	126.30	6.33	1.34
	4	124.17	122.23	123.23	1.56	0.76
	5	247.72	262.77	230.9	6.08	6.79
	6	63.90	83.47	62.42	30.63	2.32
	7	176.57	262.77	172.62	48.82	2.24
	8	379.81	413.02	359.54	8.74	5.34
500	1	52.40	56.72	53.17	8.24	1.45
	2	45.74	45.30	44.08	0.96	3.63
	3	127.27	133.48	121.46	4.88	4.57
	4	120.72	121.29	118.95	0.47	1.47
	5	240.26	278.65	246.43	15.98	2.57
	6	63.30	87.30	63.14	37.92	0.25
	7	172.88	226.00	169.69	30.73	1.85
	8	368.33	416.24	349.34	13.01	5.16

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Dynamics model updating of structures at high temperature based on novel particle swarm optimization algorithm

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