An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
This work addresses the challenge of predicting complex fluid behaviors like bifurcations for researchers in computational fluid dynamics, though it appears incremental as it builds on existing POD-NN methods.
The paper tackled the problem of modeling bifurcating fluid phenomena in computational fluid dynamics using a reduced-order approach with artificial neural networks, achieving efficient recovery of critical points and flow patterns even at high Reynolds numbers.
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 at high Reynolds numbers.