Interpolation-based reduced-order modelling for steady transonic flows via manifold learning

Thomas Franz, Ralf Zimmermann, Stefan Goertz, Niklas Karcher

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

This paper presents a parametric reduced-order model (ROM) based on manifold learning (ML) for use in steady transonic aerodynamic applications. The main objective of this work is to derive an efficient ROM that exploits the low-dimensional nonlinear solution manifold to ensure an improved treatment of the nonlinearities involved in varying the inflow conditions to obtain an accurate prediction of shocks. The reduced-order representation of the data is derived using the Isomap ML method, which is applied to a set of sampled computational fluid dynamics (CFD) data. In order to develop a ROM that has the ability to predict approximate CFD solutions at untried parameter combinations, Isomap is coupled with an interpolation method to capture the variations in parameters like the angle of attack or the Mach number. Furthermore, an approximate local inverse mapping from the reduced-order representation to the full CFD solution space is introduced. The proposed ROM, called Isomap+I, is applied to the two-dimensional NACA 64A010 airfoil and to the 3D LANN wing. The results are compared to those obtained by proper orthogonal decomposition plus interpolation (POD+I) and to the full-order CFD model.

OriginalsprogEngelsk
TidsskriftInternational Journal of Computational Fluid Dynamics
Vol/bind28
Udgave nummer3-4
Sider (fra-til)106-121
Antal sider16
ISSN1061-8562
DOI
StatusUdgivet - 1. mar. 2014
Udgivet eksterntJa

Fingeraftryk

Transonic flow
transonic flow
computational fluid dynamics
learning
interpolation
Interpolation
Computational fluid dynamics
Transonic aerodynamics
angle of attack
airfoils
Angle of attack
Airfoils
aerodynamics
Mach number
dynamic models
wings
Dynamic models
shock
nonlinearity
Decomposition

Citer dette

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Interpolation-based reduced-order modelling for steady transonic flows via manifold learning. / Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan; Karcher, Niklas.

I: International Journal of Computational Fluid Dynamics, Bind 28, Nr. 3-4, 01.03.2014, s. 106-121.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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