Team

Dr James Taylor

Assistant Professor

Biography

James is an assistant professor in propulsion and power. He completed his PhD on “Three-Dimensional Mechanisms in Compressor Flows” at the Whittle Laboratory in 2015 and was the Rolls-Royce fellow in compressor aerodynamics until 2022. He is currently a fellow of King’s College Cambridge where he teaches undergraduate engineers and proclaims the benefits of studying and researching thermofluid mechanics.

Research topics

Specialises in aerodynamic improvements to engine efficiency, electric driven propulsion, three-dimensional flow topology, tip clearance flows, machine learning techniques and in-service performance of aero engine components. His other projects include collaborations with Siemens, Reaction Engines, Blue Bear Systems Research and Lilium. He works on both experimental techniques and CFD methods.

Publications & updates

Separated Flow Topology in Compressors

Authors:

J.V. Taylor

Publication:

Journal of Turbomachinery

DOI:

https://doi.org/10.1115/1.4044132

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Complete Flow Conditioning Gauzes

This paper presents a novel method that can completely condition the flow into a turbomachinery experiment. A single, thick, 3D-printed gauze can be tailored to provide an exact stagnation pressure profile, flow angle distribution and turbulence intensity. The new method is superior to existing techniques as it provides accurate and cheap flow conditioning in just one component. It removes the requirement for separate endwall boundary layer generators, inlet guide vanes and turbulence grids. The paper is presented in two parts: first, the methods for designing complete flow conditioning gauzes are presented. In the second part, two gauzes are designed and manufactured for two compressor testing applications. Both applications demonstrate the fine control that can be achieved in an experiment using these gauzes.

Authors:

Taylor, J.V.

Publication:

Experiments in Fluids

DOI:

https://doi.org/10.1007/s00348-019-2682-9

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Predicting the Operability of Damaged Compressors Using Machine Learning

Authors:

Taylor, J., Conduit, B., Dickens, A., Hall, C., Hillel, M., & Miller, R.

Publication:

ASME Turbo Expo 2019

DOI:

https://doi.org/10.17863/CAM.38691

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Career opportunities

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