Open-source framework for parametric study of hydrofoil profiles and motivation for using Physics-Informed Neural Networks (PINN)

Grm, Aleksander (avtor), Vukašinović, Nikola (avtor)

This research presents a robust, automated, open-source computational framework for generating high throughput hydrodynamic datasets for Physics Informed Neural Network (PINN) applications. Recognising the significant computational overhead of conventional Computational Fluid Dynamics (CFD) in parametric design spaces, this study proposes an integrated approach using GMSH for algorithmic mesh generation and OpenFOAM for high-fidelity fluid flow simulations. The framework is used to conduct an extensive parametric investigation on an HPC system for three distinct hydrofoil geometries: the NACA 0012, NACA 2412, and NACA 4412 profiles. Simulations are performed across a comprehensive operational envelope, covering angles of attack (α) from −15◦ to +15◦ and Reynolds numbers (Re) from 104 to 107. The resulting structured database contains spatial distributions of velocity and pressure fields, providing the empirical foundation for a deep learning architecture developed within the PyTorch ecosystem. To ensure physical consistency, the PINN architecture enforces the governing Navier Stokes equations by incorporating the conservation law residuals directly into the composite loss function. By automating the transition from geometric definition to numerical solution, the framework provides the necessary ground truth for training and validating PINNs. The accuracy of the surrogate model is evaluated by comparing the L2 relative error and pressure coefficient (Cp) distributions with OpenFOAM steady-state results. This approach aims to accelerate the prediction of hydrodynamic performance in foil-assisted vessel analysis while ensuring that the surrogate model strictly adheres to governing physical laws. The proposed methodology offers a scalable and accessible pathway for developing rapid-response surrogate models in maritime engineering.

• Jezik: Angleški jezik
• Ključne besede: hydrodynamics, parametric studies, high-performance computing (HPC)
• Vrsta gradiva: Izvleček, povzetek
• Tipologija: 1.12 - Objavljeni povzetek znanstvenega prispevka na konferenci
• Organizacija: FPP - Fakulteta za pomorstvo in promet ; FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: Str. 60
• PID: 20.500.12556/RUL-188843
• UDK: 532.5:004.8
• COBISS.SI-ID: 275308547

Avtorske pravice

Licenca Creative Commons je navedena v kolofonu publikacije (https://ashpc.eu/event/27/attachments/154/410/ASHPC26_BOOKLET.pdf): “The abstracts in this booklet are licenced under a CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/) ”. (Datum opombe: 29. 9. 2026).

• Datum objave v RUL: 29.09.2026

Gradivo je del monografije

• Naslov: ASHPC26 : Austrian-Slovenian HPC Meeting 2026
• Uredniki: Ivan Vialov
• Kraj izida: Vienna
• Založnik: EuroCC
• ISBN: 978-3-200-10998-8
• COBISS.SI-ID: 275296259

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: hidrodinamika, parametrične analize, visokozmogljivostno računalništvo (HPC)