Prediction of the non-linear aeroelastic behavior of a cantilever flat plate and equivalent 2D model

[EN] Reducing structure weigh is one of the main strategies for decreasing environmental and manufacturing costs of engineering solutions. The reduction in material is normally related with a higher impact of aeroelastic solicitations. For some industrial cases it is needed to account for non-linear...

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Bibliographic Details
Authors: Gil, A.|||0000-0001-7192-6992, Tiseira, Andrés-Omar|||0000-0001-9472-2386, Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079, Cremades-Botella, Andrés|||0000-0002-7052-4913
Format: article
Publication Date:2021
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/182700
Online Access:https://riunet.upv.es/handle/10251/182700
Access Level:Open access
Keyword:Aeroelasticity
Computational fluid dynamics
Fluid structure interaction
Non-linear aerodynamics
Unsteady Reynolds averaged Navier Stokes
Structural dynamics
MAQUINAS Y MOTORES TERMICOS
INGENIERIA AEROESPACIAL
Description
Summary:[EN] Reducing structure weigh is one of the main strategies for decreasing environmental and manufacturing costs of engineering solutions. The reduction in material is normally related with a higher impact of aeroelastic solicitations. For some industrial cases it is needed to account for non-linear aerodynamics and, therefore, the whole fully coupled set of equations needs to be simulated in order to predict its behavior. One possible way of reducing the high computational cost associated with this problem is the use of an equivalent 2D model, whose derivation is not straightforward. This article presents a methodology for reducing the order from a complete three dimensional arbitrary beam to its equivalent 2D characteristic section. The behavior of both systems is analyzed and it is shown how, when the methodology is applied, the resulting 2D system is capable to predict similar results with a computational cost which is reduced by orders of magnitude.