The search for the gust-wing interaction "textbook"

TL;DR

This paper demonstrates that a small, carefully selected subset of gust events can effectively model complex unsteady aerodynamics, reducing data requirements while maintaining high predictive accuracy.

Contribution

It introduces a systematic approach to identify a representative 'textbook' subset from large experimental datasets for aerodynamic modeling.

Findings

A few canonical gust events can match the predictive accuracy of much larger datasets.

The methodology enhances modeling efficiency and interpretability.

Large-scale experiments can be distilled into essential representative examples.

Abstract

We address whether complex physical relations can be investigated through the synergy of automated high-volume experiments and the reduction of large datasets to a concise, representative subset of canonical examples -- a "textbook". To this end, we consider the unsteady aerodynamics of wing-gust interactions, which is characterized by its rich, high-dimensional physics. We take advantage of a purpose-built gust generator to systematically produce over 1,000 distinct random gust events and to measure the unsteady loads induced on a delta wing. We then employ a data summarization procedure to identify representative subsets of increasing size from the large-scale database, which then serve as training data for a machine-learning model of the aerodynamic loads from sparse pressure measurements. An appropriately selected "textbook" of a few events can achieve predictive accuracy comparable to random training sets up to two orders of magnitude larger, capturing the intrinsic diversity of the full-scale data and enhancing modeling efficiency and interpretability. Our methodology evidences the potential of distilling the essential information contained in large amounts of experimental observations.