01.1 - Aircraft Design and Integrated Systems (Theory, Methods and Tools)

DATA-DRIVEN FAST NOISE PREDICTION OF AN UNCONVENTIONAL AIRCRAFT USING HIGH-FIDELITY SIMULATION DATA

D. Eisenhut, Germany; A. Strohmayer, Germany

Global warming impacts aviation, and disruptive concepts are foreseen to reduce emissions. Yet in conceptual aircraft design, adequate methods are oftentimes missing, and additional risks like noise arise. This study bridges this gap by developing a machine learning model to predict noise for an unconventional concept, allowing for aircraft-level optimization.

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DOI: 10.71945/icas2026_0273