Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions

TL;DR

This paper demonstrates a physics-informed neural network approach to solving Navier-Stokes equations with hard boundary conditions, achieving accurate results without labeled data and validating against CFD simulations.

Contribution

The work introduces a data-free PINN method with exact boundary condition enforcement for Navier-Stokes equations, extending to transient flows and validating accuracy against CFD.

Findings

PINNs accurately predict steady flow profiles at low Reynolds numbers.

The method extends to transient, time-dependent flow simulations.

Normalized L2 errors range from 10^{-4} to 10^{-1} for tested cases.

Abstract

In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a powerful and robust framework for solving nonlinear differential equations across a wide range of scientific and engineering disciplines, including biology, geophysics, astrophysics and fluid dynamics. In the PINN framework, the governing partial differential equations, along with initial and boundary conditions, are encoded directly into the loss function, enabling the network to learn solutions that are consistent with the underlying physics. In this work, we employ the PINN framework to solve the dimensionless Navier-Stokes equations for three two-dimensional incompressible, steady, laminar flow problems without using any labeled data. The boundary and initial conditions are enforced in a hard manner, ensuring they are satisfied exactly rather than penalized during training. We validate the PINN predicted velocity profiles, drag coefficients and pressure profiles against the conventional computational fluid dynamics (CFD) simulations for moderate to high values of Reynolds number ($Re$). It is observed that the PINN predictions show good agreement with the CFD results at lower $Re$. We also extend our analysis to a transient condition and find that our method is equally capable of simulating complex time-dependent flow dynamics. To quantitatively assess the accuracy, we compute the $L_2$ normalized error, which lies in the range $\mathcal{O}(10^{-4})$ - $\mathcal{O}(10^{-1})$ for our chosen case studies.