A combination of large eddy simulation and physics-informed machine learning to predict pore-scale flow behaviours in fibrous porous media: A case study of transient flow passing through a surgical mask. (April 2023)

• Record Type: Journal Article
• Authors: Mesgarpour, Mehrdad; Habib, Rabeeah; Shadloo, Mostafa Safdari; Karimi, Nader
• Is Part Of: Engineering analysis with boundary elements. Volume 149(2023)
• Page Start: 52
• Page End: 70
• Publication Date: 2023-04
• Subjects: Physics-informed machine learning -- Large eddy simulation -- Pore-scale -- Fibrous porous media; ANN Artificial neural networks -- MSE Mean square error -- LES Large-eddy simulation -- ML Machine learning -- MLP Multilayer perceptron -- LPM Litter per minutes -- PIML Physics-informed machine learning -- LES Large eddy simulation; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2023.01.010
• Languages: English
• ISSNs: 0955-7997

Abstract

Abstract: A predictive method using physics-informed machine learning (PIML) and large eddy simulation (LES) is developed to capture the transient flow field through microscale porous media (PSPM). An image processing technique extracts the 3D geometry of the internal layers of the mask from 2D microscopy images, and then the fluid flow is first simulated numerically. The subsequently developed PIML method successfully predicts the transient flow patterns inside the porous medium. For the first time, 3D maps of time-dependent pressure, velocity, and vorticity are predicted across the fibrous porous medium. The results show that, compared to conventional computational fluid dynamics, the PIML method can reduce the computational cost by over 20 times. Further, the LES model can replicate the fine fluctuations caused by the flow passage through the porous medium. Therefore, the developed methodology allows for transient flow predictions in highly complex configurations at a substantially reduced cost. The results indicate that the PIML method can reduce the total computational time (including training and prediction) by 22.5 and 20.7 times over the standard numerical simulation, based on speeds of 0.1 and 0.5 m/s, respectively. Several factors including the inherent differences between CPUs and GPUs, algorithms and software, appear to influence this improvement.