不确定性下的固体火箭发动机性能精确代理建模方法
Accurate surrogate modeling method for performance of solid rocket motor under uncertainty
时茗扬 1李春娜 1刘洋 1龚春林1
作者信息
- 1. 西北工业大学 航天学院,陕西 西安 710072
- 折叠
摘要
为了在保证固体火箭可靠性的同时尽可能提高其运载能力,需要在方案设计阶段的多学科设计优化(Multidisciplinary Design Optimization,MDO)过程中精确量化发动机推力曲线的不确定性.本文针对考虑不确定性的MDO过程中推力曲线的不确定性难以精确量化以及不确定性分析效率过低的问题,提出了一种精确代理建模方法.通过本征正交分解方法实现发动机推力曲线不确定性的降维表达;建立Kriging代理模型来预测降维后模态系数的前4阶统计矩;使用最大熵法建立模态系数的精确概率分布模型,进而得到推力曲线的精确分布.对星型装药发动机的推力不确定性建模结果表明,推力不确定性分布的预测置信度可达98%;单次不确定性分析时间相比蒙特卡洛方法缩短99.92%.
Abstract
The uncertainty of thrust curve of solid rocket motor(SRM)need be accurately quantified to en-sure the reliability and improve the load capacity of solid rocket on multidisciplinary design optimization(MDO)in the scheme design phase.An accurate surrogate modeling method is proposed in this paper to solve the prob-lem of difficulty in accurately quantifying the uncertainty of thrust curves and the low efficiency of uncertainty analysis in the process of MDO considering uncertainty.First the proper orthogonal decomposition is used to real-ize the dimensional reduction of the engine thrust curve with uncertainty.Then a Kriging surrogate model is built to predict the first four statistical moments of base modal coefficients.Finally,an accurate probability distribution model of the base modal coefficients is established by the maximum entropy method.The precise distribution of the thrust curve is then calculated.The result of uncertainty trust model used on a star grain SRM shows that the predicted confidence level of thrust uncertainty distribution reaches 98%.And the time of single uncertainty anal-ysis is reduced by 99.92%compared with Monte Carlo method.
关键词
固体火箭发动机/不确定性建模/最大熵法/本征正交分解/代理模型Key words
Solid rocket motor/Uncertainty modeling/Maximum entropy method/Proper orthogonal de-composition/Surrogate model引用本文复制引用
出版年
2025
NETL
NSTL
万方数据