Enhancing Data-Assimilation in CFD using Graph Neural Networks
This work addresses data assimilation challenges in fluid mechanics for researchers and engineers, representing an incremental improvement by integrating GNNs into existing methods.
The authors tackled the problem of data assimilation in computational fluid dynamics by developing a novel approach that combines adjoint-optimization with Graph Neural Networks to model closure terms in Reynolds-Averaged Navier-Stokes equations, resulting in excellent meanflow reconstruction without feature selection and promising generalization to unseen flows.
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.