In applying porous media air bearings (PMABs), designing the pore microstructure of porous media to obtain the desired permeability is challenging. The key parameters in this design are to map the pore microstructure characteristics to permeability and adapt to manufacturing process with the characteristics. For this purpose, a framework is proposed to characterize pore microstructure with morphology descriptor and predict permeability. 3D digital images of porous media are obtained using X-ray micro-computed tomography and various image construction techniques. The complex pore microstructure of porous media is represented with a pore network. Permeability is calculated based on the pore network. Sixteen pore microstructure morphology descriptors are initially calculated to characterize pore microstructure. A back-propagation neural network (BPNN) is built to learn the correlation between morphology descriptors and permeability. Pearson correlation coefficient (PCC) and feature importance scores of morphology descriptors are obtained based on the dataset and trained BPNN. The results demonstrate that the prediction performance of BPNN is excellent. The following six morphology descriptors (porosity, coordination number, average pore diameter, average throat diameter, average pore throat ratio, average throat length) are reserved to characterize pore microstructure. Finally, two types of pore microstructure are designed with the help of knowledge obtained by this research.
Porous graphite is employed for the development of air bearings used in the ultraprecision machine tools. The static performance of this new bearing type depends on the permeability and inertia coefficient of the porous graphite inserted in it. In this study, an experimental fitting method was used in conjunction with computational fluid dynamics (CFD) modeling to determine the permeability and inertia coefficient of three types of porous graphite with porosity of 16%, 13%, and 8% respectively. The experimental results show that the Compressible-Darcy-Forchheimer equation can fit the experimental mass flow rate and pressure drop well. The average permeability of SG-3, SG-5 and SG-8 porous graphite are 5.74×10^(-14)m2, 3.65×10^(-15)m2 and 1.85×10^(-15) m2 respectively. SG-5 and SG-8 porous graphite have good permeability consistency and can be used to make porous media air bearing. The PPSM (number of pores per square millimeter) of SG-3 and SG-8 are similar, but the permeabilities are very different. For low permeability porous graphite, samples with different sizes from the same material should be tested and the averaged inertia coefficient can be used. The accuracy of the pore-scale FVM (finite volume method) is highly dependent on the quality of the pore microstructure image.
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