Department of Engineering, Cambridge

The push for net-zero power and propulsion technologies is leading to many new device architectures being proposed. Within these radial and mixed flow pumps and compressors feature heavily, often in sizes and performing duties not currently available off the shelf. A rapid design system would enable the flexibility and speed to evaluate these new architecture, accelerating the delivery of the UK Net Zero target.

This project addresses this by redeploying and extending current data-driven design methods for axial machines to cover this space. Current data-driven design tools suitable for axial machines have given an acceleration of 2 orders of magnitude, this project intends to achieve the same speed up for radial and mixed flow devices. This would provide the toolset needed to rapidly explore these new architectures and accelerate our net-zero ambitions. The project will use recently developed data-driven aerodynamic inverse design methods combining CFD of various degrees of fidelity, typically used in design, with multi-variate regression methods to provide both inverse design capabilities and flow field prediction of radial and mixed flow devices. The topic makes it well suited for any students with a shared interest in both aerodynamics and data-science.

You would be based within the Whittle laboratory, a supportive and collaborative research group. The new Whittle Laboratory building works are currently underway so you would be within the first cohort to move into the new building which accommodates both state of the art testing facilities and a vibrant community of research students, researchers, academics and industrial partners.

https://www.jobs.cam.ac.uk/job/45819/