Publication Date

3-17-2026

Document Type

Article

Publication Title

Acta Geotechnica

DOI

10.1007/s11440-026-02979-7

Abstract

This study presents a physics-informed neural network (PINN) framework to simulate axisymmetric submerged jet grout flow using high-fidelity data generated from ANSYS Fluent simulations. The CFD model solves the Reynolds-averaged Navier–Stokes (RANS) equations coupled with the k-ε turbulence closure model, converted into axisymmetric form to reflect jet grouting. The PINN incorporates continuity, RANS equations, and turbulence closure equations into the loss function, enabling it to learn turbulent flow physics without requiring mesh generation. Separate neural networks were used for each flow variables—axial velocity (U), radial velocity (V), pressure (P), turbulent kinetic energy (k), and dissipation rate (ε). The model was trained using a curriculum learning strategy to enhance stability and avoid gradient explosion and the predicted results closely matched the CFD outputs. This study demonstrates that PINNs can serve as efficient and accurate surrogate models for complex, high-gradient flow systems like jet grouting, offering a promising tool for understanding submerged jet flow behavior and reducing computational demands in geotechnical engineering.

Keywords

Computational fluid dynamic, Ground improvement, Jet grouting, Physics-informed neural networks, Turbulent flow

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Civil and Environmental Engineering

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