Geometry-aware PINNs for Turbulent Flow Prediction

TL;DR

This paper introduces a geometry-aware physics-informed neural network (PINN) surrogate model capable of predicting turbulent flow fields around NACA airfoils with unseen shapes and flow conditions, reducing the need for costly CFD simulations.

Contribution

A novel geometry-aware parametric PINN model that predicts turbulent flow for unseen airfoil shapes and conditions using a local+global embedding approach and limited CFD data.

Findings

Successfully predicts flow fields for unseen airfoil shapes.

Accurately models turbulent flows across different Reynolds numbers.

Reduces computational cost compared to traditional CFD simulations.

Abstract

Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especially in turbulent flow scenarios, which involves running costly simulations at every design iteration. While parametric RANS-PINN type approaches have been proven to make effective turbulent surrogates, as a means of predicting unknown Reynolds number flows for a given geometry at near real-time, geometry aware physics informed surrogates with the ability to predict varying geometries are a relatively less studied topic. A novel geometry aware parametric PINN surrogate model has been created, which can predict flow fields for NACA 4 digit airfoils in turbulent conditions, for unseen shapes as well as inlet flow conditions. A local+global approach for embedding has been proposed, where known global design parameters for an airfoil as well as local SDF values can be used as inputs to the model along with velocity inlet/Reynolds number ($\mathcal{R}_e$) to predict the flow fields. A RANS formulation of the Navier-Stokes equations with a 2-equation k-epsilon turbulence model has been used for the PDE losses, in addition to limited CFD data from 8 different NACA airfoils for training. The models have then been validated with unknown NACA airfoils at unseen Reynolds numbers.