Projection-based and neural-net reduced order model for nonlinear Navier–Stokes equations

Autor:	Hoang Huy Nguyen, Chin Chun Ooi, My Ha Dao, Quang Tuyen Le
Rok vydání:	2021
Předmět:	Nonlinear autoregressive exogenous model Discretization Artificial neural network business.industry Applied Mathematics Computational fluid dynamics Projection (linear algebra) Nonlinear system Modeling and Simulation Applied mathematics Galerkin method business Navier–Stokes equations Mathematics
Zdroj:	Applied Mathematical Modelling. 89:1294-1315
ISSN:	0307-904X
DOI:	10.1016/j.apm.2020.07.023
Popis:	This paper presents complete steps for the construction and solution algorithm for a projection-based reduced order model coupled with an artificial neural net model. The reduced model is constructed based on the Galerkin projection of the equations governing the physical processes on reduced subspaces obtained by the Proper Orthogonal Decomposition method. The projection is done using numerical discretisation schemes implemented in the commonly used OpenFOAM® platform. The neural net model is trained using the Nonlinear AutoRegressive eXogenous model network (NARX) architecture. The reduced order models are demonstrated for true predictions of incompressible flows at low Reynolds numbers driven by various boundary conditions. Compared with the full CFD simulations, this model shows excellent agreement while only requiring a fraction of the computational resources.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f1e4ac46e08b436eb89045a4c79d2d85 https://doi.org/10.1016/j.apm.2020.07.023 Zobrazit plný text záznamu Full Text from ScienceDirect