Data-driven aerodynamic models for aeroelastic simulations

Lelkes, János; Horvath, Daniel; Lendvai, Bálint; Farkas, Balázs; Bak, Bendegúz Dezső; Kalmár‐Nagy, Tamás

doi:10.1016/j.jsv.2023.117847

Cited by 2 publications

(1 citation statement)

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“…FEM-based aeroelastic models are resource intensive, but provide accurate results and are relatively simple to set up. Some authors are analyzing novel approaches to aeroelasticity modeling based on neural networks [19][20][21]. These models are based only on previously acquired aeroelasticity data and do not require any physics-based calculations once they are developed.…”

Section: Introductionmentioning

confidence: 99%

Aeroelasticity Model for Highly Flexible Aircraft Based on the Vortex Lattice Method

Dagilis,

Kilikevičius

2023

Aerospace

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With the increasing use of composite materials in aviation, structural aircraft design often becomes limited by stiffness, rather than strength. As a consequence, aeroelastic analysis becomes more important to optimize both aircraft structures and control algorithms. A low computational cost aeroelasticity model based on VLM and rigid-body dynamics is proposed in this work. UAV flight testing is performed to evaluate the accuracy of the proposed model. Two flight sections are chosen to be modeled based on recorded aerodynamic surface control data. The calculated accelerations are compared with recorded flight data. It is found that the proposed model adequately captures the general flight profile, with acceleration peak errors between −6.2% and +8.4%. The average relative error during the entire flight section is 39% to 44%, mainly caused by rebounds during the beginning and end of pull-up maneuvers. The model could provide useful results for the initial phases of aircraft control law design when comparing different control algorithms.

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Section: Introductionmentioning

confidence: 99%