Enhancing Data-Assimilation in CFD using Graph Neural Networks
- URL: http://arxiv.org/abs/2311.18027v1
- Date: Wed, 29 Nov 2023 19:11:40 GMT
- Title: Enhancing Data-Assimilation in CFD using Graph Neural Networks
- Authors: Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini,
Onofrio Semeraro
- Abstract summary: We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models.
We obtain our results using direct numerical simulations based on a Finite Element Method (FEM) solver; a two-fold interface between the GNN model and the solver allows the GNN's predictions to be incorporated into post-processing steps of the FEM analysis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a novel machine learning approach for data assimilation applied in
fluid mechanics, based on adjoint-optimization augmented by Graph Neural
Networks (GNNs) models. We consider as baseline the Reynolds-Averaged
Navier-Stokes (RANS) equations, where the unknown is the meanflow and a closure
model based on the Reynolds-stress tensor is required for correctly computing
the solution. An end-to-end process is cast; first, we train a GNN model for
the closure term. Second, the GNN model is introduced in the training process
of data assimilation, where the RANS equations act as a physics constraint for
a consistent prediction. We obtain our results using direct numerical
simulations based on a Finite Element Method (FEM) solver; a two-fold interface
between the GNN model and the solver allows the GNN's predictions to be
incorporated into post-processing steps of the FEM analysis. The proposed
scheme provides an excellent reconstruction of the meanflow without any
features selection; preliminary results show promising generalization
properties over unseen flow configurations.
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