Benjamin Rothwell

Contact

• workRoom B107 Coates
  University Park
  Nottingham
  NG7 2RD
  UK
• work011574 87227/ 86411
• Benjamin.Rothwell@nottingham.ac.uk

Biography

Ben Rothwell is an assistant professor in the department of Mechanical, Materials and Manufacturing Engineering at the University of Nottingham. He received his Ph.D. from the University of Leeds in 2016. From 2016 till 2018, he was a Research Associate with the Mechanical and Aerospace Systems (MAS), (formally the Gas Turbine and Transmissions Research Centre) at the University of Nottingham. Prior to joining the University of Nottingham in 2016, he was a Aero-science instructor at the King Faisal Air Academy in Riyadh, Saudi Arabia. His main research interests include multi physics computational modeling of journal bearings supported by experimental rigs.

Expertise Summary

Thermo Elastic- Hydrodynamic lubrication (TEHL)

Multiphysics modeling

Journal and tilting pad Bearings

Machine and deep learning techniques

Recent Publications

• KUMAR, A., MASE, J. M., RENGASAMY, D., ROTHWELL, B., TORRES, M. T., WINKLER, D. A. and FIGUEREDO, G. P., 2023. EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python
• LAYTON, J., ROTHWELL, B. C., AMBROSE, S., EASTWICK, C., MEDINA, H. and REBELO, N., 2023. A New Thermal Elasto-Hydrodynamic Lubrication Solver Implementation in OpenFOAM: Lubricants Lubricants. 11(7),
• RENGASAMY, D., MASE, J. M., KUMAR, A., ROTHWELL, B., TORRES, M. T., ALEXANDER, M. R., WINKLER, D. A. and FIGUEREDO, G. P., 2022. Feature importance in machine learning models: A fuzzy information fusion approach: Neurocomputing Neurocomputing. 511, 163-174
• BELL, V., ARCE, I. M., MASE, J. M., RENGASAMY, D., ROTHWELL, B. and FIGUEREDO, G. P., 2022. Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding: SAE International Journal of Aerospace SAE International Journal of Aerospace. 15(2),

• View all publications

• KUMAR, A., MASE, J. M., RENGASAMY, D., ROTHWELL, B., TORRES, M. T., WINKLER, D. A. and FIGUEREDO, G. P., 2023. EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python
• LAYTON, J., ROTHWELL, B. C., AMBROSE, S., EASTWICK, C., MEDINA, H. and REBELO, N., 2023. A New Thermal Elasto-Hydrodynamic Lubrication Solver Implementation in OpenFOAM: Lubricants Lubricants. 11(7),
• RENGASAMY, D., MASE, J. M., KUMAR, A., ROTHWELL, B., TORRES, M. T., ALEXANDER, M. R., WINKLER, D. A. and FIGUEREDO, G. P., 2022. Feature importance in machine learning models: A fuzzy information fusion approach: Neurocomputing Neurocomputing. 511, 163-174
• BELL, V., ARCE, I. M., MASE, J. M., RENGASAMY, D., ROTHWELL, B. and FIGUEREDO, G. P., 2022. Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding: SAE International Journal of Aerospace SAE International Journal of Aerospace. 15(2),
• SHABBIR, S., GARVEY, S. D., DAKKA, S. M. and ROTHWELL, B. C., 2021. An experimental study of contact temperatures at sealing interface against varying shaft surfaces: Coatings Coatings. 11(2), 1-20
• SHABBIR, S., GARVEY, S. D., DAKKA, S. M. and ROTHWELL, B. C., 2021. Seal failure in aerospace applications – creating a global open-source database: Sealing Technology Sealing Technology. 2021(2), 7-9
• RENGASAMY, D., ROTHWELL, B. C. and FIGUEREDO, G. P., 2021. Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion: Applied Sciences (Switzerland) Applied Sciences (Switzerland). 11(24),
• RENGASAMY, D., ROTHWELL, B. and FIGUEREDO, G. P., 2020. Asymmetric Loss Functions for Deep Learning Early Predictions of Remaining Useful Life in Aerospace Gas Turbine Engines