Quantification of Margins and Uncertainties for Aerospace Systems using Stochastic Expansions
Quantification of Margins and Uncertainties for Aerospace Systems using Stochastic Expansions
复制标题
使用随机展开式量化航空航天系统的裕度和不确定性
DOI：
10.2514/6.2014-0682
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
T. Winter
中科院分区：
文献类型：
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作者：
Thomas K. West;S. Hosder;T. Winter
本研究的目的是演示使用随机扩展在量化的利润和复杂的航空航天系统的不确定性。在这项研究中，随机扩展，基于非侵入式多项式混沌，被用来有效地表示不确定性的设计指标和相关的性能限制的系统。此外，还概述了分析性能指标和性能限值之间包含不同不确定性类型的系统的程序。这些方法证明了三个模型问题，每个具有混合（认识和偶然）的不确定性，这是通过使用二阶概率模型传播。第一个是高度非线性分析函数的复杂系统。第二个是航天器重返大气层的多系统物理模型。性能度量包括两个系统，用于确定最大过载、必要的倾斜角修正和沿沿着轨道的最大对流热载荷。最后一个模型是一个多学科模型的设计和分析的高速民用运输。总体而言，这项工作的方法和实例详细介绍了复杂航空航天系统的可靠性测量方法，以及量化裕度和不确定性对可靠系统设计的重要性。
The objective of this study was to demonstrate the use of stochastic expansions in the quantification of margins and uncertainties in complex aerospace systems. In this study, stochastic expansions, based on nonintrusive polynomial chaos, were utilized for efficient representation of uncertainty both in design metrics and associated performance limits of a system. Additionally, procedures were outlined for analyzing systems that contain different uncertainty types between the performance metrics and performance limits. These methodologies were demonstrated on three model problems, each possessing mixed (epistemic and aleatory) uncertainty, which was propagated through the models using second-order probability. The first was a complex system of highly nonlinear analytical functions. The second was a multisystem, physics based model for spacecraft reentry. The performance metrics consisted of two systems used to determine the maximum g-load, the necessary bank angle correction, and maximum convective heat load along a reentry trajectory. The last model was a multidisciplinary model for the design and analysis of a High Speed Civil Transport. Overall, the methodologies and examples of this work have detailed an approach for measuring the reliability of complex aerospace systems as well as the importance of quantifying margins and uncertainties for the design of reliable systems.