Reduction of Outflow Boundary Influence on Aerodynamic Performance using Neural Networks

TL;DR

This paper introduces neural network techniques to improve outflow boundary conditions in lattice Boltzmann simulations, significantly enhancing accuracy and reducing computational costs in aerodynamic predictions.

Contribution

It proposes two novel NN-based boundary condition methods, including direct particle distribution reconstruction and dynamic tuning of characteristic boundary parameters.

Findings

Improved accuracy over traditional boundary conditions in 2D flow over a cylinder.

Accurate aerodynamic predictions for NACA0012 airfoil using NN-enhanced boundary conditions.

Superior performance of NN-tuned characteristic boundary conditions in vortex simulations.

Abstract

The accurate treatment of outflow boundary conditions remains a critical challenge in computational fluid dynamics when predicting aerodynamic forces and/or acoustic emissions. This is particularly evident when employing the lattice Boltzmann method (LBM) as the numerical solution technique, which often suffers from inaccuracies induced by artificial reflections from outflow boundaries. This paper investigates the use of neural networks (NN) to mitigate these adverse boundary effects and enable truncated domain requirements. Two distinct NN-based approaches are proposed: (1) direct reconstruction of unknown particle distribution functions at the outflow boundary; and (2) enhancement of established characteristic boundary conditions (CBC) by dynamically tuning their parameters. The direct reconstruction model was trained on data generated from a 2D flow over a cylindrical obstruction. The drag, lift, and Strouhal number were used to test the new boundary condition. We analyzed results for various Reynolds numbers and restricted domain sizes where it demonstrated significantly improved predictions when compared with the traditional Zou & He boundary condition. To examine the robustness of the NN-based reconstruction, the same condition was applied to the simulation of a NACA0012 airfoil, again providing accurate aerodynamic performance predictions. The neural-enhanced CBC were evaluated on a 2D convected vortex benchmark and showed superior performance in minimizing density errors compared to CBCs with fixed parameters. These findings highlight the potential of NN-integrated boundary conditions to improve accuracy and reduce computational expense of aerodynamic and acoustic emissions simulations with the LBM.