Zimmerman and Weissenburger's flutter margin method is widely used to estimate the aeroelastic coalescence flutter speed. In contrast to aeroelastic decay rates, the flutter margin exhibits monotonic decay with respect to airspeed redering it effective in extrapolating the flutter speed using flight test data conducted at pre-flutter airspeeds. This paper reports the generalization of the Bayesian formulation of the flutter margin method by Khalil et al. developed to tackle measurement and modeling uncertainties. This paper improves the predictive performance of the previous algorithm by incorporating the joint prior of aeroelastic modal frequencies and decay rates among airspeeds in order to better estimate the joint posterior of modal parameters using observational data. The modal parameter prior is constructed using the classical two-degree-of-freedom pitch-plunge aeroelastic model whose system matrices (e.g. structural stiffness and damping matrices) vary randomly. Such joint modal parameter prior enforces statistical dependence among posteriors of modal parameters and the associated flutter margins across airspeeds. Numerical studies demonstrate a considerable reduction of uncertainties on the predicted flutter speed obtained from the generalized Bayesian flutter margin method. This improved algorithm can cut cost by reducing the number of flight tests and better assess the uncertainty against aeroelastic flutter.
翻译:Zimmerman 和 Weissenburger 的飞速边距法被广泛用来估计气动弹性凝固速度的震动速度。 与气动弹性衰减率相反, 飞动边距在气速变速方面呈现了单体衰变, 利用在气流前的空中速度进行的飞行试验数据外推滑速度。 本文报告了Khalil等人为解决测量和建模不确定性而开发的滑动边距法的巴耶斯配方的概括性。 本文改进了先前算法的预测性能, 结合了空气速度中的气动模型频率和衰变速率, 以便更好地利用观测数据估算模型参数的联合外表外表。 先前的模型参数是使用传统的两度自由小球阵列的气压模型, 系统矩阵( 如结构坚硬度和测距矩阵) 随机变化不一, 在模型参数的后, 将以前的算法参数和相关的气流边际差差之间的相对统计依赖性进行更好的统计性调整, 将预测性压压压压在空中飞行速度上。