Prediction and uncertainty propagation of correlated time-varying quantities using surrogate models
Prediction and uncertainty propagation of correlated time-varying quantities using surrogate models
复制标题
使用代理模型预测相关时变量和不确定性传播
作者：
I. Tartaruga;J. E. Cooper;M. Lowenberg;Pia N Sartor;S. Coggon;Y. Lemmens
相关量的识别在工程和物理的几个领域是特别有趣的，例如在可靠结构设计的发展中。当“时变”量被分析时，对相关的有趣量（iq），例如弯矩，扭矩等，可以通过绘制它们彼此的关系来显示，并且由包络的极值（凸包）决定的临界条件。本文提出了一种基于降阶奇异值的建模技术，能够快速计算出需要考虑设计参数变化影响的系统的相关负荷包络线。将该方法推广到有效地量化系统参数中不确定性的影响。以某民用客机的气动弹性数值模型为例，验证了该方法的有效性。
The identification of correlated quantities is of particular interest in several fields of engineering and physics, for example in the development of reliable structural designs. When ‘time-varying’ quantities are analysed, pairs of correlated interesting quantities (IQs), e.g. bending moments, torques, etc., can be displayed by plotting them against each other, and the critical conditions determined by the extreme values of the envelope (convex hull). In this paper, a reduced order singular value-based modelling technique is developed that enables a fast computation of the correlated loads envelope for systems where the effect of variation of design parameters needs to be considered. The approach is extended to efficiently quantify the effects of uncertainty in the system parameters. The effectiveness of the method is demonstrated by consideration of the gust loads occurring from the aeroelastic numerical model of a civil jet airliner.