论文标题
一种稳健的动态模式分解方法，用于经过透射冲击自助餐的机翼
A robust dynamic mode decomposition methodology for an airfoil undergoing transonic shock buffet
论文作者
论文摘要
动态模式分解（DMD）是一种数据驱动的技术，用于分析和建模包括跨性别自助餐流量在内的流体问题。尽管具有优势，但DMD仍对所选设置和所采用数据的特征遭受敏感性。在这项工作中，我们仔细检查了上述敏感性，确定可能的陷阱，并提供最佳实践，以在表现出跨性别电击自助力的流动上进行鲁棒性DMD。具体而言，我们评估了各种DMD变体，并测试它们对POD界定截断和采样率的敏感性。我们分析的关键推动因素是DMD算法作为一个模块化框架的新介绍，该框架由五个不同的步骤组成。当采样率太低时，这些测试还突出了现有的混叠危险。最后，提供了有关如何在跨声音自助餐流程上准确，可靠地执行DMD的实用建议和准则的列表。
Dynamic mode decomposition (DMD) is a data-driven technique widely used to analyze and model fluid problems including transonic buffet flows. Despite its strengths, DMD is known to suffer from sensitivities to the selected settings and the characteristics of the employed data. In this work, we closely examine the aforementioned sensitivities, identify possible pitfalls, and provide best practices to robustly perform DMD on a flow exhibiting transonic shock buffet. Specifically, we assess various DMD variants and test their sensitivity to the POD rank truncation and the sampling rate. A critical enabler to our analysis is a new presentation of the DMD algorithm as a modular framework consisting of five distinct steps. The tests also highlight the existing dangers of aliasing, when the sampling rate is too low. Finally, a list of practical recommendations and guidelines on how to accurately and robustly perform DMD on a transonic buffet flow is provided.
