Exemplar-based objective classification of gust-induced loads across multiple flight conditions

2026-08-12

AI Summary

This study addresses the lack of objective and interpretable criteria for classifying gust load responses by proposing a machine learning approach based on representative samples. Leveraging representation learning and data summarization algorithms, the method extracts a minimal yet highly representative subset from 3,480 experimental datasets of flying-wing models. By integrating similarity metrics with cluster analysis, it establishes an objective classification framework that transcends specific flight conditions. The resulting taxonomy identifies nine fundamental response types, each characterized by distinct transient features that shed light on underlying, shared aerodynamic mechanisms. This interpretable classification not only facilitates expert analysis but also provides a principled basis for guiding subsequent high-fidelity experiments.

Abstract

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.

Problem

• gust-induced loads
• objective classification
• flight conditions
• exemplar-based
• fluid mechanics

Innovation

• exemplar-based classification
• gust-induced loads
• machine-learned representation
• similarity-based clustering
• flight condition generalization