Enhancing CFD solver with Machine Learning techniques

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Sousa, P (Author) Other; Rodrigues, CV (Author) Other; Afonso, AM (Author) FEUP

Journal

Title: Computer Methods in Applied Mechanics and Engineering

Vol. 429

ISSN: 0045-7825

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-010-KEZ

DOI: 10.1016/j.cma.2024.117133

Abstract (EN): This study addresses the computational challenges in fluid flow simulations arising from demanding computational grids, required to capture the temporal and length scales involved. Our approach focuses on the pressure solver, as this is a resource-intensive component in Computational Fluid Dynamics (CFD) solvers. We achieve this by integrating a Machine Learning (ML) surrogate model with an incompressible fluid flow solver. We created two variants of an ML-enhanced CFD solver which were able to reduce the number of iterations required by the CFD pressure solver during unsteady flow simulations. Consequently, the simulations yielded comparable drag coefficients and Strouhal numbers, accompanied by an eightfold decrease in execution time. The performance enhancements are attributed to reduced computational effort per temporal iteration and early-stage forcing on the simulation dynamical behavior when using the ML-based surrogate models. This research introduces an approach to enhance the computational efficiency of fluid flow analyses by incorporating surrogate models to aid the pressure solver in CFD simulations. We propose a Hybrid CFD solver, ie. a physics-informed solver enhanced with data-driven surrogate models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17