Year: 2025
Type: conference-paper
Abstract: In the aerospace field, the long-term stable operation of high-speed rotor components relies on the performance of the lubrication system, where the seepage behavior of lubricant directly affects the oil supply capacity of shaft oil reservoirs and bearing cages, while porous polyimide materials have become an ideal choice for optimizing such systems due to their high-temperature resistance and self-lubricating properties. To address the insufficient accuracy in pore modeling of porous polyimide (PPI) materials, this study proposes a modified four-parameter stochastic growth method (QSGS) integrating scanning electron microscopy (SEM) images and mercury intrusion porosimetry data. Experimental results demonstrate that the corrected model achieves a porosity of 40.60%, showing merely a 0.32% deviation from the measured value (40.92%). The pore heterogeneity index exhibits a 7.42% error. By establishing a grid point-nanoscale mapping relationship (1:60 nm), the deviation of critical pore size distribution (≤1 μm) from experimental data reduces to 1.60%. This approach constructs a parameter calibration framework combining morphological features and statistical principles, while its cross-scale modeling capability provides an accuracy-controllable digital benchmark for multiphysics simulations of porous materials.
Authors: Zhaoliang Dou, Penghao Li, Gang Zhou, Shaohua Zhang, Fengbin Liu
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414931933