Enhancing Data-Assimilation in CFD using Graph Neural Networks

TL;DR

This paper introduces a novel machine learning approach combining Graph Neural Networks with adjoint-optimization to improve data assimilation in fluid mechanics, specifically enhancing the accuracy of RANS-based meanflow predictions.

Contribution

It develops an end-to-end framework integrating GNNs into RANS data assimilation, demonstrating improved meanflow reconstruction and generalization over unseen flow configurations.

Findings

Excellent meanflow reconstruction without feature selection

Promising generalization to unseen flow configurations

Integration of GNNs with FEM solver for fluid dynamics

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.