Corpus ID: 237592730

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

@article{Pichi2021AnAN,
  title={An artificial neural network approach to bifurcating phenomena in computational fluid dynamics},
  author={Federico Pichi and Francesco Ballarin and Gianluigi Rozza and Jan S. Hesthaven},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.10765}
}
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 NavierStokes 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… Expand
2 Citations
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This work explores the development and the analysis of an efficient reduced order model for the study of a bifurcating phenomenon, known as the Coandă effect, in a multi-physics setting involving fluid and solid media, and provides several insights on how the introduction of an elastic structure influences the bifURcating behaviour. Expand
A hybrid partitioned deep learning methodology for moving interface and fluid–structure interaction
In this work, we present a hybrid partitioned deep learning framework for the reduced-order modeling of moving interfaces and predicting fluid-structure interaction. Using the discretizedExpand

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