All Outputs (3)

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings (2024)
Journal Article
Cartwright, S., Rothwell, B., 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, 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.

An empirical critique of the low income low energy efficiency approach to measuring fuel poverty (2024)
Journal Article
Semple, T., Rodrigues, L., Harvey, J., Figueredo, G., Nica-Avram, G., Gillott, M., …Goulding, J. (2024). An empirical critique of the low income low energy efficiency approach to measuring fuel poverty. Energy Policy, 186, Article 114014. https://doi.org/10.1016/j.enpol.2024.114014

Fuel poverty is a complex socioenvironmental issue of increasing global significance. In England, fuel poverty is assessed via the Low Income Low Energy Efficiency (LILEE) indicator, yet concerns exist regarding the efficacy of this metric given its... Read More about An empirical critique of the low income low energy efficiency approach to measuring fuel poverty.

Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon (2023)
Journal Article
Memon, H., Gjerde, E., Lynam, A., Chowdhury, A., De Maere, G., Figueredo, G., & Hussain, T. (2024). Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon. Engineering Applications of Artificial Intelligence, 128, Article 107465. https://doi.org/10.1016/j.engappai.2023.107465

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is benefici... Read More about Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon.