All Outputs (10)

Numerical study on the aeroacoustics and interaction of two distributed-propulsion propellers in co- and counter-rotations (2022)
Journal Article
Sharma, S., Huang, G., Ambrose, S., & Jefferson-Loveday, R. (2022). Numerical study on the aeroacoustics and interaction of two distributed-propulsion propellers in co- and counter-rotations. Journal of the Acoustical Society of America, 152(4), A52-A52. https://doi.org/10.1121/10.0015514

Driven by increasing demands for a sustainable and eco-friendly future in aviation, distributed electric propulsion (DEP) systems have received much attention for their high aerodynamic efficiency. DEP systems of lower noise emissions are desired by... Read More about Numerical study on the aeroacoustics and interaction of two distributed-propulsion propellers in co- and counter-rotations.

COUPLED EULERIAN THIN FILM MODEL AND LAGRANGIAN DISCRETE PHASE MODEL TO PREDICT FILM THICKNESS INSIDE AN AERO-ENGINE BEARING CHAMBER (2022)
Presentation / Conference Contribution
Mouvanal, S., Singh, K., Jefferson-Loveday, R., Ambrose, S., Eastwick, C., Johnson, K., & Jacobs, A. (2022). COUPLED EULERIAN THIN FILM MODEL AND LAGRANGIAN DISCRETE PHASE MODEL TO PREDICT FILM THICKNESS INSIDE AN AERO-ENGINE BEARING CHAMBER. In Proceedings of Global Power and Propulsion Society. https://doi.org/10.33737/gpps22-tc-68

In aero-engine bearing chambers, two-phase shearing flows are difficult to predict as Computational Fluid Dynamics (CFD) RANS models tend to overestimate interfacial turbulence levels, leading to inaccuracies in the modelling of the flow. Turbulence... Read More about COUPLED EULERIAN THIN FILM MODEL AND LAGRANGIAN DISCRETE PHASE MODEL TO PREDICT FILM THICKNESS INSIDE AN AERO-ENGINE BEARING CHAMBER.

High Fidelity Cfd-Trained Machine Learning To Inform Rans-Modelled Interfacial Turbulence (2022)
Presentation / Conference Contribution
Bertolotti, L., Jefferson-Loveday, R., Ambrose, S., & Korsukova, E. (2022). High Fidelity Cfd-Trained Machine Learning To Inform Rans-Modelled Interfacial Turbulence. In Proceedings of Global Power and Propulsion Society. https://doi.org/10.33737/gpps22-tc-30

In aero-engine bearing chambers, two-phase shearing flows are difficult to predict as Computational Fluid Dynamics (CFD) RANS models tend to overestimate interfacial turbulence levels, leading to inaccuracies in the modelling of the flow. Turbulence... Read More about High Fidelity Cfd-Trained Machine Learning To Inform Rans-Modelled Interfacial Turbulence.

Numerical simulation of multi-scale oil films on a rotating cup using VOF and coupled Eulerian thin-film-dpm approaches (2022)
Presentation / Conference Contribution
Nicoli, A., Singh, K., Jefferson-Loveday, R., Ambrose, S., & Mouvanal, S. (2022). Numerical simulation of multi-scale oil films on a rotating cup using VOF and coupled Eulerian thin-film-dpm approaches. . https://doi.org/10.1115/GT2022-82533

In this study, the newly developed Nottingham Gas Turbine and Transmission Research Centre (G2TRC) Bearing Chamber Test Module is presented and investigated computationally. The module houses a rotating cup and shaft configuration in order to simulat... Read More about Numerical simulation of multi-scale oil films on a rotating cup using VOF and coupled Eulerian thin-film-dpm approaches.

Experimental Investigation Into Oil Shedding From a Rotating Cup Geometry (2022)
Presentation / Conference Contribution
Cageao, P. P., Johnson, K., Ambrose, S., & Omar, R. (2022). Experimental Investigation Into Oil Shedding From a Rotating Cup Geometry. In Proceedings of the ASME 2022 Turbomachinery Technical Conference & Exposition Turbo Expo 2022 June 13-17, 2022, Rotterdam, The Netherlands. https://doi.org/10.1115/GT2022-82195

Oil shedding from rotating components and static elements within rotating systems has a number of applications within the design and understanding of transmission systems as well as wider industrial applications. There is a reasonable body of prior r... Read More about Experimental Investigation Into Oil Shedding From a Rotating Cup Geometry.

Numerical Investigation Into Maximum Pressure Capability of Intershaft Hydraulic Seals (2022)
Presentation / Conference Contribution
Warusevitane, A., Johnson, K., & Ambrose, S. (2022). Numerical Investigation Into Maximum Pressure Capability of Intershaft Hydraulic Seals. In Proceedings of ASME Turbo Expo 2022 Turbomachinery Technical Conference and Exposition GT2022 June 13-17, 2022, Rotterdam, The Netherlands. https://doi.org/10.1115/GT2022-82196

Hydraulic seals are used as intershaft seals in aero-engines and consist of an oil filled trough on the higher speed shaft and a fin on the lower speed shaft that dips into the oil forming the seal. Rotation is imparted to the sealing fluid within th... Read More about Numerical Investigation Into Maximum Pressure Capability of Intershaft Hydraulic Seals.

Investigation of Droplet Shedding in an Aeroengine Bearing Chamber Using Convolutional Neural Networks (2022)
Presentation / Conference Contribution
Farbrother, K., Cageao, P. P., Johnson, K., & Ambrose, S. (2022). Investigation of Droplet Shedding in an Aeroengine Bearing Chamber Using Convolutional Neural Networks. In Proceedings of the ASME 2022 Turbomachinery Technical Conference & Exposition GT2022 June 13-17, 2022, Rotterdam, The Netherlands. https://doi.org/10.1115/GT2022-82275

More efficient aeroengines are needed to reduce the environmental impact of civil aviation. Design changes for increased efficiency mean that higher loads are transmitted through the bearings supporting the rotating shafts. Better bearing chamber des... Read More about Investigation of Droplet Shedding in an Aeroengine Bearing Chamber Using Convolutional Neural Networks.

Predictions of falling wavy films based on the depth averaged thin film model and its application to aeroengine bearing chamber (2022)
Presentation / Conference Contribution
Singh, K., Nicoli, A., Jefferson-Loveday, R., Ambrose, S., Paleo Cageao, P., Johnson, K., …Jacobs, A. (2022). Predictions of falling wavy films based on the depth averaged thin film model and its application to aeroengine bearing chamber. . https://doi.org/10.1115/gt2022-78010

In the present study, the evolution of a falling wavy film with upstream forced excitation is investigated using the depth averaged thin film model, known as Eulerian Thin Film Model (ETFM). Because of the depth averaging of the governing equations,... Read More about Predictions of falling wavy films based on the depth averaged thin film model and its application to aeroengine bearing chamber.

Numerical characterisation of noise generated by a distributed-propulsion propeller (2022)
Presentation / Conference Contribution
Sharma, S., Huang, G., Ambrose, S., & Jefferson-Loveday, R. (2022). Numerical characterisation of noise generated by a distributed-propulsion propeller. . https://doi.org/10.2514/6.2022-2878

The shift towards sustainable and eco-friendly future of aviation is further catalysed by the aggressive targets set by administrative and industrial stakeholders. It has emerged as one of the solutions with the potential to increase overall efficien... Read More about Numerical characterisation of noise generated by a distributed-propulsion propeller.

Numerical Simulation of Multi-Scale Oil Films on a Rotating Cup Using VOF and Coupled Eulerian Thin-Film-DPM Approaches (2022)
Presentation / Conference Contribution
Nicoli, A., Singh, K., Jefferson-Loveday, R., Ambrose, S., & Mouvanal, S. (2022). Numerical Simulation of Multi-Scale Oil Films on a Rotating Cup Using VOF and Coupled Eulerian Thin-Film-DPM Approaches. In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. https://doi.org/10.1115/gt2022-82533

In this study, the newly developed Nottingham Gas Turbine and Transmission Research Centre (G2TRC) Bearing Chamber Test Module is presented and investigated computationally. The module houses a rotating cup and shaft configuration in order to simulat... Read More about Numerical Simulation of Multi-Scale Oil Films on a Rotating Cup Using VOF and Coupled Eulerian Thin-Film-DPM Approaches.