A Neural Network-Based Feature Recognition Method in Adaptive Refinement for Efficient Reynolds Equation Solving
Year: 2025
Type: article
Abstract: This study proposes an adaptive refinement method based on feature recognition to achieve rapid solution of the Reynolds equation. Leveraging an isogeometric analysis (IGA) solution framework supporting local refinement, three natural refinement features tailored for solving the Reynolds equation in fluid lubrication including two physical features, pressure value and pressure gradient and an element size feature for discretization are introduced firstly to identify mesh elements. Then a neural network model is trained on feature data to predict element classifications effectively. Finally, this model is integrated into the adaptive refinement solution framework and validated through simulations. Comparative validation was conducted on two distinct Reynolds equation instances, with results demonstrating that the proposed algorithm can effectively evaluate refinement regions globally, avoiding issues such as mesh non-conformity often caused by conventional independent element marking algorithms. The distribution of degrees of freedom is more rational, and the parallel prediction model enhances the speed of the refinement solution.
Source: Lubricants
Authors: Gang Rao, Lei Ma, Bingquan Zuo, Jiashu Yang, Weikang Xie
Institution: Wuhan University of Science and Technology
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413721583