Rotorcraft low-noise trajectories design: black-box optimization using surrogates
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11081_2022_9781_ReferencePDF.pdf
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6.26 MB
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Author(s)
Dieumegard, Pierre
Cafieri, Sonia
Delahaye, Daniel
Hansman, R. J.
Date Issued
January 20, 2023
Publisher
Springer US
Citation
Dieumegard, Pierre, Cafieri, Sonia, Delahaye, Daniel and Hansman, R. J. 2023. "Rotorcraft low-noise trajectories design: black-box optimization using surrogates."
Version
Author's final manuscript
Abstract
Abstract
This paper addresses the noise-minimal trajectory optimization problem for a specific type of aircraft: rotorcraft. It relies on a realistic noise footprint computation software provided by industry that is black-box. Locally optimal trajectories are computed through a tailored solution approach based on the Mesh-Adaptive Direct Search algorithm. We propose multiple surrogates defined according to our knowledge of the problem, including a surrogate relying on the physics of the problem (approximating the rotorcraft noise model), and another based on a machine learning (neural network) method. The proposed solution approach is further enhanced by the computation of an appropriate starting guess through a path planning algorithm tailored to the problem, and by the reduction of the variable space domain. The performance of the proposed methodology both in terms of quality of the solutions (trajectories exhibiting significant noise reduction compared to those currently flown in practice) and computing time is illustrated through numerical experiments on real-world case studies.
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11081-022-09781-w
