含区间分布参数的尾喷管调节机构可靠性分析

张政; 王攀; 周瀚渊

doi:10.13700/j.bh.1001-5965.2022.0089

含区间分布参数的尾喷管调节机构可靠性分析

doi: 10.13700/j.bh.1001-5965.2022.0089

张政¹,
王攀^1, ,,
周瀚渊²

1.
西北工业大学力学与土木建筑学院，西安 710072
2.
西安航天动力研究所，西安 710100

基金项目: 国家自然科学基金(51975473)

详细信息

通讯作者:
E-mail：panwang@nwpu.edu.cn

中图分类号: V434.2；V431
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- 被引次数: 0
出版历程
- 收稿日期: 2022-02-25
- 录用日期: 2022-06-06
- 网络出版日期: 2022-08-29
- 整期出版日期: 2023-12-29

Reliability analysis of nozzle adjustment mechanism with interval distribution parameters

1.
School of Mechanics，Civil Engineering and Architecture，Northwestern Polytechnical University，Xi’an 710072，China
2.
Xi’an Aerospace Propulsion Institute，Xi’an 710100，China

Funds: National Natural Science Foundation of China (51975473)

More Information

Corresponding author: E-mail：panwang@nwpu.edu.cn

摘要

摘要:
为提高尾喷管调节机构的可靠性分析效率，提出一种结合拒绝采样和主动学习Kriging代理模型的分析方法。在ADAMS中建立了某发动机尾喷管调节机构虚拟样机仿真模型，通过运动学分析对所建模型进行验证；考虑其输入变量含区间分布参数的情形，建立基于调节机构定位精度的极限状态函数；引入主动学习Kriging代理模型，在分布参数随机变化的情况下，通过拒绝采样方法来捕捉样本空间的变化，从而构建适用于整个样本空间内的Kriging代理模型。通过数值算例验证所提方法的可行性，并采用所提方法对调节机构失效概率的上下限进行了计算分析，为提高区间分布参数下的可靠性分析效率提供了一种新的思路。
- 调节机构 /
- Kriging代理模型 /
- 拒绝采样 /
- 区间分布参数 /
- 可靠性
Abstract:
To improve the reliability analysis efficiency of the engine nozzle adjustment mechanism, an analysis method combining rejection sampling and active learning Kriging surrogate model is proposed. A virtual prototype simulation model of an engine nozzle adjustment mechanism was established in ADAMS, and the established model is verified by kinematics analysis. Considering the situation that its input variables contain interval distribution parameters, a limit state function based on the positioning accuracy of the adjusting mechanism is established. When distribution parameters change at random, the rejection sampling approach captures the changes in the sample space in order to build a Kriging surrogate model that is appropriate for the full sample space. A numerical example that validates the viability of the suggested approach is used to calculate and analyze the upper and lower boundaries of the adjustment mechanism failure probability. It provides a new method to improve the reliability analysis efficiency under interval distributed parameters.
- adjustment mechanism /
- Kriging surrogate model /
- rejection sampling /
- interval distribution parameter /
- reliability

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图 1 尾喷管调节机构模型示意图

Figure 1. Schematic diagram of model of nozzle adjustment mechanism

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图 2 调节机构虚拟样机模型

Figure 2. Adjustment mechanism virtual prototype model

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图 3 调节机构平面简化图

Figure 3. Simplified plan of adjustment mechanism

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图 4 调节机构t时刻状态

Figure 4. State of adjustment mechanism at time t

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图 5 鱼鳞片展开角度随时间变化对比

Figure 5. Comparison of fish scale expansion angle with time

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图 6 拒绝采样方法示意图

Figure 6. Schematic diagram of rejection sampling method

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图 7 调节机构可靠性分析流程

Figure 7. Adjustment mechanism reliability analysis process

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图 8 悬臂梁结构

Figure 8. Cantilever beam structure

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图 9 数值算例代理模型样本点更新过程

Figure 9. Update process of sample points of surrogate model of numerical example

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图 10 数值算例候选样本池大小随迭代次数的变化

Figure 10. Variation of size of candidate sample pool with number of iterations in numerical example

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图 11 调节机构代理模型样本点更新过程

Figure 11. Update process of sample points of surrogate model of adjustment mechanism

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图 12 候选样本池大小随迭代次数的变化

Figure 12. Variation of size of candidate sample pool with number of iterations in adjustment mechanism

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表 1 输入变量分布参数

Table 1. Input variable distribution parameters

变量	销轴B半径X₁/mm	销轴C半径X₂/mm	销轴B处摩擦系数X₃	销轴C处摩擦系数X₄	阻力矩X₅/(N·m)
均值	[2.65, 2.75]	[2.65, 2.75]	[0.08, 0.12]	[0.08, 0.12]	[55.92, 60.92]
标准差	0.05	0.05	0.005	0.005	1.120

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表 2 悬臂梁问题随机变量分布参数

Table 2. Random variable distribution parameters for cantilever problem

变量	单位载荷$ \omega $/(N·m⁻²)	梁长$L$/m	截面尺寸$b$/mm
均值	[900,1100]	[5,7]	[220,280]
标准差	100	0.9	37.5

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表 3 数值算例可靠性分析结果

Table 3. Reliability analysis results of numerical example

方法	失效概率(下限)/10⁻⁴	失效概率(下限)误差/%	失效概率(上限)	失效概率(上限)误差/%	计算时间/s	样本量	候选样本池规模
MCS	4.2		0.2640
AK-MCS	4.7	11.90	0.2646	0.22	733	15+107	31×10⁵
本文方法	4	4.76	0.2643	0.11	124	15+80	92528

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表 4 调节机构可靠性分析结果

Table 4. Reliability analysis results of adjustment mechanism

失效概率(下限)	失效概率（上限）
4×10⁻⁵	0.0158

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