| 引用本文: | 聂兆伟,王浩,秦梦,等.混合不确定条件下的飞行器级间分离可靠性分析.[J].国防科技大学学报,2022,44(3):104-111.[点击复制] |
| NIE Zhaowei,WANG Hao,QIN Meng,et al.Reliability analysis of flight vehicle stage separation under mixed uncertainties[J].Journal of National University of Defense Technology,2022,44(3):104-111[点击复制] |
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| 混合不确定条件下的飞行器级间分离可靠性分析 |
| 聂兆伟1,2,王浩1,秦梦1,张海瑞1 |
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(1. 中国运载火箭技术研究院, 北京 100076;2. 南京理工大学 机械学院, 江苏 南京 210094)
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| 摘要: |
| 为量化飞行器级间分离过程随机不确定性和认知不确定性的综合影响,结合概率和区间理论混合模型特点,提出基于随机和区间理论混合模型的飞行器级间分离可靠性分析方法。面向高超声速飞行器分离任务需求,建立分离动力学仿真模型,针对级间分离结构的几何特点,设计快速碰撞检测方法,进而构建分离任务的可靠性分析混合模型。通过将该模型转化为随机可靠性分析的无约束优化问题,考虑分离过程中复杂外力及力矩导致功能函数高度非线性的特点,利用高效全局优化和主动学习Kriging方法实现无约束优化问题高效求解。结合实例表明,该方法能够准确描述混合不确定性因素对飞行器分离过程的影响,给出了飞行器分离任务可靠性区间,可为飞行器分离方案的精细化设计提供决策支持。 |
| 关键词: 混合可靠性分析 碰撞检测模型 高效全局优化 自主学习Kriging方法 |
| DOI：10.11887/j.cn.202203013 |
| 投稿日期：2020-12-13 |
| 基金项目:国家部委基金资助项目(JSZL2020203B001) |
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| Reliability analysis of flight vehicle stage separation under mixed uncertainties |
| NIE Zhaowei1,2, WANG Hao1, QIN Meng1, ZHANG Hairui1 |
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(1. China Academy of Launch Vehicle Technology, Beijing 100076, China;2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)
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| Abstract: |
| To quantity the comprehensive influence of both random and epistemic uncertainties during the process of flight vehicle stage separation, combined with the characteristics of hybrid model based on random and interval theory, a new reliability assessment method of separation between flight vehicle stages based on the hybrid model of random and interval theory was proposed. According to the requirements of the hypersonic vehicle separation mission, the separation kinetic simulation model was constructed. According to the geometric characteristic of the separation structure between the stages, a new rapid collision detection method was proposed. And the hybrid reliability assessment model of flight vehicle separation mission was constructed. The hybrid reliability assessment model was converted to unconstrained optimization problem of random reliability assessment. Considering of the characteristic of highly nonlinearity of system performance function due to complex external force and moment during the process of flight vehicle stage separation, the unconstrained optimization problem was efficiently solved by efficient global optimization and active learning Kriging method. It is shown that the influence of mixed uncertainty factors on the flight vehicle separation process can be described exactly through this method and the interval of flight vehicle separation mission reliability can be given accurately, which can further support the detailed design of flight vehicle separation. |
| Keywords: mixed reliability analysis collision detection model efficient global optimization active learning Kriging method |
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