A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Cartwright, Samuel; Rothwell, Benjamin C.; Figueredo, Grazziela; Medina, Humberto; Eastwick, Carol; Layton, James; Ambrose, Stephen

Abstract

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39 000 000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10 000 rpm, while load values are varied between 1 and 30 kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4 % or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1 % error at up to 200 % of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10 000, enabling real-time predictions of lubrication systems.

Citation

Cartwright, S., Rothwell, B. C., Figueredo, G., Medina, H., Eastwick, C., Layton, J., & Ambrose, S. (2024). A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings. Tribology International, 196, Article 109670. https://doi.org/10.1016/j.triboint.2024.109670

• Journal Article Type: Article
• Acceptance Date: Apr 14, 2024
• Online Publication Date: Apr 17, 2024
• Publication Date: 2024-08
• Deposit Date: Apr 22, 2024
• Publicly Available Date: May 7, 2024
• Journal: Tribology International
• Print ISSN: 0301-679X
• Electronic ISSN: 1879-2464
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 196
• Article Number: 109670
• DOI: https://doi.org/10.1016/j.triboint.2024.109670
• Keywords: Journal bearing; Hydrodynamic lubrication; Machine learning; Neural network; PyTorch; OpenFOAM
• Public URL: https://nottingham-repository.worktribe.com/output/33838641
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0301679X24004225?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings; Journal Title: Tribology International; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.triboint.2024.109670; Content Type: article; Copyright: © 2024 The Author(s). Published by Elsevier Ltd.