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arXiv Machine Learning By Ashray Saxena, Sharad Shrivastava

How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning

Read the original on arXiv Machine Learning →

The paper investigates the folded transonic flutter boundary of Isogai’s case A aerofoil, whose elastic axis is positioned half a chord ahead of the leading edge. Using coupled unsteady Euler simulations, a Gaussian‑process surrogate trained via batch active learning, and split conformal prediction, the authors map how the boundary changes as the elastic axis is moved, revealing that the fold exists only when the axis is more than ~0.4 chord ahead and closes between 0.39 and 0.33 chord ahead. Energy budget analyses explain the lower and upper branches of the fold, showing how shock phase and damping of the supersonic surface govern the flutter behavior.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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