Mesh adaptation for unsteady problems via Reduced Order Modeling

Fossati, Marco; Najafiyazdi, Mostafa; Habashi, Wagdi G.

doi:10.2514/6.2011-3692

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Local Reduced-order Modeling Via Machine Learning1

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2015

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“…For unsteady problems, the proposed method would still be applicable, given that, in the context of nonintrusive ROM, time can be directly incorporated as one of the input parameters of the analysis. An example of the application of this approach in the context of unsteady problems can be found in [25]. It is known, however, that by using POD, the temporal dynamics of the system may not be reproduced accurately; therefore, alternatives to POD should be considered in the case of unsteady flows [26].…”

Section: Local Reduced-order Modeling Via Machine Learningmentioning

confidence: 98%

Local Reduced-Order Modeling and Iterative Sampling for Parametric Analyses of Aero-Icing Problems

Zhao

Habashi

Fossati³

2015

AIAA Journal

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A framework of local reduced-order modeling using machine learning algorithms is presented together with an approach to optimally select the snapshots for strongly nonlinear problems. By using an unsupervised learning algorithm, solutions are grouped into clusters of similar features. The input parameter space is divided into subregions by decision boundaries based on a supervised learning algorithm. Local reduced-order bases are extracted on each cluster, for which the solutions are represented as a linear combination of the basis vectors from their corresponding subregion. The proposed methodology is employed to conduct a comprehensive, exploration of the inflight icing certification envelopes.

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Section: Local Reduced-order Modeling Via Machine Learningmentioning

confidence: 98%