Fusion of advanced testing and machine learning methods in propulsion aerodynamics PhD

Applications are invited for a fully funded PhD studentship in the area of aero-engine aerodynamics within the Propulsion Engineering Centre at Cranfield University sponsored by EPSRC and LaVision UK.

Cranfield University has been at the forefront of propulsion system engineering and research for the last decades. The requirement to meet NetZero targets for future aircraft configurations, has inspired the development of novel architectures which are known to require a much closer coupling between the engine and the airframe. A key part of this integration is linked to the interactions between the air intake and the compression system which are hugely dependent on the aerodynamic compatibility between these two systems.

Currently, intakes and engines are developed independently, with very little knowledge exchange between these two design streams during the design process. Compatibility tests are typically conducted at a later stage to only reveal potentially significant flaws in the performance of the integrated system whose remedy can cause notable delays and cost penalties to the development programme. To improve this design and development process, detailed knowledge about the fan performance across a range of possible perturbations that may come from an intake system is needed very early in the design process, when typically the intake geometry is not finalised. As a result, a flexible method to verify engine stability in absence of an inlet system is required, that will allow the exposure of the fan to representative, flow perturbations that intakes typically generate.

During this project, a novel method is envisaged, whereby intake representative flow profiles will be synthetically generated by combinations of simpler flows. The first focus of this research is on the methods and characteristics of generating bespoke distortion patterns. The target flow profiles will be determined using previous knowledge from experimentally determined flow characteristics within engine intakes. A methodological approach to reproduce these via arrays of vortex generators will then be developed via appropriately trained Artificial Intelligence (AI) models. Within this context, this research will investigate if AI can be deployed to design or recommend VG array to produce prescribed patterns. This is a major change in the design process relative to the current state-of-the-art. The outcomes of the AI-generated flow profiles will finally be experimentally validated via stereo Particle Image Velocimetry methods in one of the existing test rigs within the Centre.

This PhD work will be sponsored by the Engineering and Physical Sciences Research Council - Doctoral Training Partnership (EPSRC – DTP) and LaVision in the UK. LaVision has proven expertise on measurement solutions, providing world class knowledge and delivering cutting-edge science and technology in non-intrusive methods for flow field characterisation with high resolution in space and time.

It is expected that throughout this PhD opportunity, a new experimental capability will be developed to generate bespoke distortion patterns that could be encountered in complex aero-engine intakes. By deploying state-of-the-art AI and machine learning techniques, the experimental data will be further exploited to synthesise inlet flow patterns that can be used for industrial testing as part of engine development and certification programmes.

This PhD opportunity includes a range of opportunities for the successful applicant to present the work in national and international conferences as well as to participate in training courses.

The successful applicant will gain an in-depth understanding of experimental aerodynamics, propulsion integration and machine learning techniques. The student will also gain an understanding in data analytics and reduced-order modelling. Furthermore, the PhD candidate will gain a unique understanding of the field as well as a transferable skill-set, which will enhance a future career in industry or academia.

Sponsored by EPSRC, LaVision UK and Cranfield University. This opportunity is open to UK and international students.