Aeroelasticity & Structural Dynamics in a Fast Changing World
17 – 21 June 2024, The Hague, The Netherlands
Home Program Author Index Search

Flight flutter test modal identification by using nature inspired algorithms: A classical mathematical modeling


Go-down ifasd2024 Tracking Number 71

Presentation:
Session: Flutter testing
Room: Room 1.1
Session start: 13:30 Thu 20 Jun 2024

Demian Gomes da Silva   demian.gomes@embraer.com.br
Affifliation: Embraer S.A

Guilherme Castrignano Tavares   guilherme.tavares@embraer.com.br
Affifliation: Embraer S.A


Topics: - Flight Flutter Testing of Aircraft (Experimental methods)

Abstract:

The aviation industry is coming under increasing pressure from governments, regulatory organizations and the general public to reduce emissions. To address this, the industry has come together and pledged to cut net carbon emissions to zero by 2050. To help meet this goal, EMBRAER is exploring a wide range of bold but viable aircraft designs in the Energia concepts, evaluating a range of sustainable concepts to carry up to 50 passengers, considering a number of energy sources, propulsion architectures and airframe layouts. In addition, the company is active member of FutPrint50 Project together with an international consortium of universities, SMEs and organizations to accelerate the technologies needed to deliver a hybrid-electric aircraft that could enter service in 2035-40. From aeroelastic standpoint, the Green Aviation will impose additional challenges in design, development and certification, mainly from lighter structures in unusual configurations, with multiple engines and more electrification. Consequently, it is important the evolution of aeroelastic industrial process in both prediction and testing. The present paper is focused on an innovative approach to Flight Flutter Testing modal identification by using Nature Inspired Algorithms to solve the nonlinear identification problem without using gradients. The performance of this innovative approach is valuated based on theoretical benchmarks and real Flight Flutter Testing data. The results encourage the future use of more representative, adaptive or complex mathematical models during identification process.