AI-based predictive approach via FFB propagation in a driven-cavity of Ostwald de-Waele fluid using CFD-ANN and Levenberg-Marquardt

Ahmed Refaie Ali; Rashid Mahmood; Atif Asghar; Afraz Hussain Majeed; Mohamed H Behiry

doi:10.1038/s41598-024-60401-2

AI-based predictive approach via FFB propagation in a driven-cavity of Ostwald de-Waele fluid using CFD-ANN and Levenberg-Marquardt

Sci Rep. 2024 May 14;14(1):11024. doi: 10.1038/s41598-024-60401-2.

Authors

Ahmed Refaie Ali¹, Rashid Mahmood², Atif Asghar², Afraz Hussain Majeed³, Mohamed H Behiry⁴

Affiliations

¹ Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El Kom, 32511, Menofia, Egypt. ahmed.refaie@science.menofia.edu.eg.
² Department of Mathematics, Air University, PAF Complex E-9, Islamabad, 44000, Pakistan.
³ School of Energy and Power Engineering, Jiangsu University, Zhenjiang, 212013, China.
⁴ Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City, 11829, Egypt.

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only.

Keywords: Artificial intelligence (AI); Artificial neural networks (ANNs); Driven cavity; Finite element method (FEM); Hybrid CFD-ANN; Levenberg Marquardt Algorithm; Ostwald de-Waele fluid.