发表机构
Institute of Fluid Mechanics, TU Braunschweig（布伦瑞克工业大学流体力学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多飞行条件下阵风诱导载荷的分类问题，提出基于机器学习表示与范例选择的方法，在3480次飞翼模型压力载荷测量数据中发现9种跨姿态响应类型，可辅助理解流体力学机制。
AI 中文摘要
是否有可能找到一种客观分类准则，用于整理多飞行条件下阵风诱导载荷的复杂性，且该准则仍保持与基于飞行姿态等粗略参数的标注相当的可解释性？本文方法通过机器学习表示对大量实验观测结果进行编码，并应用总结程序选取具有高度显著性的最小范例子集。这些范例为所有观测结果提供了基于相似度的客观分类准则，便于专家检查，也可成为更精细实验的研究对象。我们在包含3480次随机阵风诱导的飞翼模型压力载荷测量数据的数据库上验证该方法，覆盖6种飞行姿态，发现9种在多种姿态间重复出现的基本响应类型，对某一类型瞬态响应的分析可深化对 underlying 流体力学的物理直觉。
英文摘要
Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type's transient response enables physical intuition into the underlying fluid mechanics.
Comments13 pages, 6 figures
