TY - JOUR
T1 - An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
AU - Pichi, Federico
AU - Ballarin, Francesco
AU - Rozza, Gianluigi
AU - Hesthaven, Jan S.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Bifurcation analysis
KW - Computational fluid dynamics
KW - Navier–Stokes equations
KW - Reduced order modelling
KW - Artificial neural network
KW - Bifurcation analysis
KW - Computational fluid dynamics
KW - Navier–Stokes equations
KW - Reduced order modelling
UR - http://hdl.handle.net/10807/225407
U2 - 10.1016/j.compfluid.2023.105813
DO - 10.1016/j.compfluid.2023.105813
M3 - Article
SN - 0045-7930
SP - 105813-N/A
JO - COMPUTERS & FLUIDS
JF - COMPUTERS & FLUIDS
ER -