2026-10-13T11:59:46Z https://www.scidb.cn/oai

10.57760/sciencedb.0124w 2026-09-10T14:51:20Z

2026-09-10 Structure Generation and Seepage Performance Analysis of Porous Oil Containing Media with Variable Porosity Gradient Distribution doi:10.57760/sciencedb.0124w en To elucidate the influence of different porosity distributions on the seepage performance of oil-bearing media, a stochastic growth method of variable-gradient porous structures, governed by regulation functions, was developed. This approach enabled the generation of porous architectures with porosity varying along the height direction, including linear gradient structures (positive and negative gradients) and parabolic gradient structures (convex and concave types). The lattice Boltzmann method (LBM) was employed to analyze the seepage behavior of lubricating oil within the porous medium. The results demonstrate that, under identical porosity conditions, the heterogeneity of pore distribution exerts a pronounced effect on fluid transport. Specifically, the average seepage velocities in both positive and negative linear gradient structures were lower than those in homogeneous porous structures, while the velocity in the negative gradient configuration was significantly higher than in its positive counterpart. Furthermore, an increased amplitude of linear porosity variation was found to deteriorate seepage performance. In contrast, parabolic gradient distributions exhibited enhanced permeability, with convex and concave structures achieving average seepage velocity improvements of 44.6% and 61.7%, respectively, compared with the homogeneous case. Notably, the convex gradient structure exhibited a distinctive "center-accelerated and edge-retarded" seepage behavior, conferring a marked advantage in sustaining lubrication performance. porous oil-containing medium; regulatory function; variable gradient pore distribution; lattice Boltzmann method; permeability Wang Xiaoyi Xu Hongbo Zhang Guotao Wang Huashun Wang Huixing PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset http://www.doi.org/10.16078/j.tribology.2025177 Science Data Bank