Development of Reduced Order Modeling Approaches for Nonlinear Aeroelastic Analyses

• Grant number: 24/01647-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2024
• Status: Discontinued
• Field of knowledge: Engineering - Aerospace Engineering - Aerodynamics
• Principal Investigator: João Luiz Filgueiras de Azevedo
• Grantee: Ana Cristina Neves Carloni
• Host Institution: Pró-Reitoria de Pós-Graduação e Pesquisa. Instituto Tecnológico de Aeronáutica (ITA)
• Company: Ministério da Defesa (Brasil). Instituto Tecnológico de Aeronáutica (ITA). Divisão de Engenharia Mecânica (IEM)
• Associated research grant: 21/11258-5 - Engineering Research Center for the Aerial Mobility of the Future (ERC-AMF), AP.PCPE
• Associated scholarship(s): 25/05625-6 - Development of Reduced Order Modeling Approaches for Nonlinear Aeroelastic Analyses, BE.EP.DR

Abstract

The main contribution of the research project proposed here is the development of aerodynamic reduced-order models that would allow routine nonlinear aeroelastic analyses to be carried out in industry. There is interest in using computational fluid dynamics (CFD) techniques to develop the aerodynamic operator for aeroelastic analyses, as this approach would enable aerodynamic nonlinearities to be included in the aeroelastic study. However, given the computing power currently available, the direct use of CFD simulations for aeroelastic analyses at each point along the flight envelope is cost prohibitive for routine engineering applications. One possible strategy for addressing this challenge is the use of reduced-order model (ROM) formulations. Especially in transonic flows, aerodynamic nonlinearities are essential for the correct prediction of unsteady aerodynamic loads, which are necessary to adequately describe the fluid-structure interaction. This research project will, therefore, consist of carrying out a detailed study of the approaches available for developing reduced-order models based on CFD data, with the aim of enabling completely nonlinear aeroelastic analyses. System identification techniques based on aerodynamic transfer functions, Koopman theory, Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), and neural networks will be explored in this study. The results obtained in this project will provide relevant information for the development and application of CFD techniques for comprehensive aeroelastic studies of aircraft configurations. (AU)