Pressure Gradient Computation for Foams With Different Geometric Properties: Based on Ann and SVR Machine Learning Model and Trained by CFD Simulations

Abstract

In this research work, a combination of computational fluid dynamics (CFD) simulation and artificial intelligence (AI) methods are conducted to study the effects of geometric properties of aluminum foams on airflow and to compute and predict pressure gradients in foams with such varied geometric parameters as porosity (65-90%) and pore diameter (200-2000 μm). The 3D foam structures are created by the Laguerre-Voronoi tessellations method. Based on the CFD results, pressure gradient for 114 different foams can be calculated in terms of inlet flow velocity (in the range 0.1-8 m/s). Foam pressure gradient is found to increase with increasing inlet flow velocity but with decreasing pore diameter and porosity. Comparisons reveal that the results obtained in the present study for pressure gradient are consistent with the data reported in the literature. It is, therefore, concluded that CFD simulation is a useful tool for pressure gradient estimation in a variety of foam types. Unique simulations are, however, needed each time foam structural properties change, which entails significant increases in the associated computation costs. This drawback may, nonetheless, be at least partially addressed by taking advantage of soft computing methods such as machine learning (ML). Artificial neural network (ANN) and support vector regression (SVR) as subsets of AI are designed (models with input variables inlet velocity and the foam structural parameters: porosity, pore diameter, and strut diameter) and trained using CFD results to predict pressure gradients in a large number of foams. When applied to new foam samples, the ML models exhibit an acceptable performance in predicting pressure gradients. Using such provisions, the method can be effectively used for predicting pressure gradient in various porous media at minimum computation costs.