Results for "Unsteady Aerodynamics" : Scipedia

WCCM-APCOM 2022

Modeling Freely Flying Monarch Butterflies Using a Strongly Coupled High Fidelity Numerical Framework

Abstract

Flying insects are impressive creatures due in part to their small size and agile flight maneuvers. Additionally, butterflies can be highly efficient fliers, as evidenced by monarchs having the longest migration amongst insects. To begin uncovering the complex mechanisms enabling [...]

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ECCOMAS Congress 2022 - 8th European Congress on Computational Methods in Applied Sciences and Engineering

Surrogate modeling of unsteady aerodynamic loads acting on a plunging airfoil

Abstract

Cost-effective parameteric surrogate models of unsteady aerodynamic loads acting on a flapping wing are highly desirable. They would enable real time aerodynamic load prediction, multiobjective optimisation and optimal control of intelligent flapping wing flight devices. In the [...]

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ECCOMAS Congress 2022 - 8th European Congress on Computational Methods in Applied Sciences and Engineering

Deep-learning based aeroelastic modeling for transient flows and non-linear dynamics

Abstract

Supervised deep learning algorithms are investigated as reduced order models for the prediction of unsteady forces resulting from flow-induced vibrations of an elastically mounted structures. Several machine learning architectures, namely convolutional, echo state, and long-term memory networks, are deployed to model the transient aerodynamic force and the motion of the structure in the presence of nonlinear dynamics due to cubic structural forces or due to aerodynamic nonlinearities such as moving shock waves and vortex-shedding.

First, machine learning architectures are designed to handle the regression problem where the motion of the structure is learned only in the transient phase. A comparative study is performed to evaluate the accuracy of the prediction of limit cycle oscillations obtained for the case of the pitch and plunge airfoil problem[1], where a nonlinear cubic torsional stiffness is considered in order to observe sub- or super-critical Hopf bifurcations in the airfoil motion[2]. Then, a hybrid neural network architecture is proposed in order

to parametrize the bifurcation diagram of the aeroelastic system across multiple coefficients in the nonlinear restoring force.

Finally, the present deep learning algorithm is employed as a meta-model to perform ensemble-based variational data assimilation[3] where pressure sensors, located in the wake flow, are used to identify the structural parameters of the spring-mass system subjected to flow-induced vibrations such as gallop and flutter mechanisms at low Reynolds number.

References

[1] J-C. Chassaing, C-T. Nitschke, D. Lucor, P. Cinnella, A. Vincenti, Advances in parametric and model-form uncertainty quantification in canonical aeroelastic systems, AerospaceLab Journal, AL14-07, Issue 14, Sep. 2018

[2] K. Li, J. Kou, W. Zhang, Deep Neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers , Nonlinear Dynamics, Vol. 96(5), pp. 1--21, 2019.

[3] V. Mons, J.-C. Chassaing, T. Gomez, P. Sagaut, Reconstruction of unsteady viscous flows using data assimilation schemes, Journal of Computational Physics, Vol. 316, pp. 255--280, 2016.

Abstract

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