Applying Physics-Informed Neural Networks to Solve Navier–Stokes Equations for Laminar Flow around a Particle

Journal Article · Fri Oct 13 00:00:00 EDT 2023 · Mathematical and Computational Applications

DOI:https://doi.org/10.3390/mca28050102· OSTI ID:2202337

Hu, Beichao ; McDaniel, Dwayne

In recent years, Physics-Informed Neural Networks (PINNs) have drawn great interest among researchers as a tool to solve computational physics problems. Unlike conventional neural networks, which are black-box models that “blindly” establish a correlation between input and output variables using a large quantity of labeled data, PINNs directly embed physical laws (primarily partial differential equations) within the loss function of neural networks. By minimizing the loss function, this approach allows the output variables to automatically satisfy physical equations without the need for labeled data. The Navier–Stokes equation is one of the most classic governing equations in thermal fluid engineering. This study constructs a PINN to solve the Navier–Stokes equations for a 2D incompressible laminar flow problem. Flows passing around a 2D circular particle are chosen as the benchmark case, and an elliptical particle is also examined to enrich the research. The velocity and pressure fields are predicted by the PINNs, and the results are compared with those derived from Computational Fluid Dynamics (CFD). Additionally, the particle drag force coefficient is calculated to quantify the discrepancy in the results of the PINNs as compared to CFD outcomes. The drag coefficient maintained an error within 10% across all test scenarios.

• Sponsoring Organization: USDOE
• Grant/Contract Number: FE0031904
• OSTI ID: 2202337
• Journal Information: Mathematical and Computational Applications, Journal Name: Mathematical and Computational Applications Vol. 28 Journal Issue: 5; ISSN 2297-8747
• Publisher: MDPI AG
• Country of Publication: Country unknown/Code not available
• Language: English