All Outputs (4)

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings (2024)
Journal Article
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

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... Read More about A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings.

A New Thermal Elasto-Hydrodynamic Lubrication Solver Implementation in OpenFOAM (2023)
Journal Article
Layton, J., Rothwell, B. C., Ambrose, S., Eastwick, C., Medina, H., & Rebelo, N. (2023). A New Thermal Elasto-Hydrodynamic Lubrication Solver Implementation in OpenFOAM. Lubricants, 11(7), Article 308. https://doi.org/10.3390/lubricants11070308

Designing effective thermal management systems within transmission systems requires simulations to consider the contributions from phenomena such as hydrodynamic lubrication regions. Computational fluid dynamics (CFD) remains computationally expensiv... Read More about A New Thermal Elasto-Hydrodynamic Lubrication Solver Implementation in OpenFOAM.

Feature importance in machine learning models: A fuzzy information fusion approach (2022)
Journal Article
Rengasamy, D., Mase, J. M., Kumar, A., Rothwell, B., Torres, M. T., Alexander, M. R., …Figueredo, G. P. (2022). Feature importance in machine learning models: A fuzzy information fusion approach. Neurocomputing, 511, 163-174. https://doi.org/10.1016/j.neucom.2022.09.053

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpreta... Read More about Feature importance in machine learning models: A fuzzy information fusion approach.

Analysis of the tribological interaction of a polytetrafluoroethylene-lined radial lip oil seal, shaft and lubricant sample (2021)
Journal Article
Shabbir, S., Garvey, S. D., Dakka, S. M., Rothwell, B. C., Su, R., Leach, R., & Weston, N. (2022). Analysis of the tribological interaction of a polytetrafluoroethylene-lined radial lip oil seal, shaft and lubricant sample. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 236(1), 123-143. https://doi.org/10.1177/13506501211005876

To investigate the tribological interaction and wear mechanisms of polytetrafluoroethylene-lined radial lip oil seals in service, a sleeve, seal and lubricant sample taken off a rotating rig are studied. The test was terminated at 72 h, after severe... Read More about Analysis of the tribological interaction of a polytetrafluoroethylene-lined radial lip oil seal, shaft and lubricant sample.