This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach dealing with non-smooth solutions set of nonlinear parametrized PDEs. Thus, we study the Navier–Stokes equations describing: (i) the Coanda effect in a channel, and (ii) the lid driven triangular cavity flow, in a physical/geometrical multi-parametrized setting, considering the effects of the domain’s configuration on the position of the bifurcation points. Finally, we propose a reduced manifold-based bifurcation diagram for a non-intrusive recovery of the critical points evolution. Exploiting such detection tool, we are able to efficiently obtain information about the pattern flow behaviour, from symmetry breaking profiles to attaching/spreading vortices, even in the advection-dominated regime.

Pichi, F., Ballarin, F., Rozza, G., Hesthaven, J. S., An artificial neural network approach to bifurcating phenomena in computational fluid dynamics, <<COMPUTERS & FLUIDS>>, 2023; (254): 105813-N/A. [doi:10.1016/j.compfluid.2023.105813] [https://hdl.handle.net/10807/225407]

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

Ballarin, Francesco;
2023

Abstract

This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach dealing with non-smooth solutions set of nonlinear parametrized PDEs. Thus, we study the Navier–Stokes equations describing: (i) the Coanda effect in a channel, and (ii) the lid driven triangular cavity flow, in a physical/geometrical multi-parametrized setting, considering the effects of the domain’s configuration on the position of the bifurcation points. Finally, we propose a reduced manifold-based bifurcation diagram for a non-intrusive recovery of the critical points evolution. Exploiting such detection tool, we are able to efficiently obtain information about the pattern flow behaviour, from symmetry breaking profiles to attaching/spreading vortices, even in the advection-dominated regime.
2023
Inglese
Pichi, F., Ballarin, F., Rozza, G., Hesthaven, J. S., An artificial neural network approach to bifurcating phenomena in computational fluid dynamics, <<COMPUTERS & FLUIDS>>, 2023; (254): 105813-N/A. [doi:10.1016/j.compfluid.2023.105813] [https://hdl.handle.net/10807/225407]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/225407
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