Abstract This work presents a flutter prediction approach that uses regression cokriging metamodels of generalized aerodynamic influence coefficients with adaptive sampling based on propagated model uncertainty along the flutter boundary. The use of regression cokriging models is compared to cokriging and regression cokriging with reinterpolation, as well as their single-fidelity counterparts. Comparisons to direct quantity-of-interest-based metamodeling are also shown. Several infill criteria based on the propagated flutter speed uncertainty are demonstrated on common flutter test cases. The value of adaptive sampling, multiple fidelity levels, and metamodeling of intermediate quantities is investigated by quantifying average cost and error metrics for the cases. Scalability with the number of structural modes is also investigated to gauge how the approach might fare for more conventional aircraft. Overall, the main benefits seen in this work stem from modeling intermediate quantities, with direct modeling costing six to eight times as much for multifidelity approaches, and three to five times as much for the single-fidelity comparators. In addition, using multiple fidelities was more accurate and required fewer infill points for convergence, leading to a cost savings of roughly 25% to 70%, depending on the case.

中文翻译：

---

使用回归协同克里金法和自适应采样进行多保真颤振预测

摘要 这项工作提出了一种颤振预测方法，该方法使用基于沿颤振边界传播的模型不确定性的自适应采样的广义空气动力学影响系数的回归协同克里金法元模型。将回归协同克里金模型的使用与带重新插值的协同克里金法和回归协同克里金法以及它们的单保真对应物进行比较。还显示了与直接基于兴趣量的元建模的比较。在常见的颤振测试案例中展示了几个基于传播颤振速度不确定性的填充标准。通过量化案例的平均成本和误差指标，研究了自适应采样、多保真度水平和中间量元建模的价值。还研究了结构模式数量的可扩展性，以衡量该方法对更传统的飞机的影响。总体而言，这项工作中看到的主要好处源于对中间量进行建模，直接建模的成本是多保真方法的六到八倍，单保真比较器的成本是三到五倍。此外，使用多个保真度更准确，收敛所需的填充点更少，根据具体情况，可节省大约 25% 到 70% 的成本。