Claudia Ibáñez García is a research student at Cranfield University. Her research focuses on the application of advanced experimental and machine-learning techniques to propulsion aerodynamics, with particular emphasis on aero-engine intake flows and distortion phenomena.

The scope of the project includes the development and application of advanced testing methodologies, flow-field measurement techniques, data analysis, and machine-learning approaches for the characterisation and prediction of complex aerodynamic flow structures. 

A key aspect of the work is the fusion of high-fidelity experimental measurements with machine-learning models to accelerate engine-intake interaction testing and assessment. This includes research into vortex-dominated flow structures, intake distortion, and methodologies that improve the integration of experimental and computational approaches.

The aim of the project is to develop data-driven methodologies that enhance the reproducibility and assessment of propulsion aerodynamic flows, enabling more efficient testing and improved prediction of engine–intake interactions.

Project title: Fusion of advanced testing and machine learning methods in propulsion aerodynamics