Artificial Intelligence (AI) allows the rapid processing of images. In the context of gas turbines this means that scans and video imaging of engine parts could be automatically assessed for variations in shape that can have significant impact of fuel consumption and engine life. In this project, data from Rolls-Royce will be combined with the latest AI and data mining tools, CAD descriptions, geometry morphing and 3D analysis codes to inform engineers on the impact of variations in geometry on engine performance. The aim is both to design parts that are more resistant to performance degradation and also to inform service teams of the best repair and replacement strategies for engines currently in use.
Combined with the latest GPU hardware, Deep Learning and Data Mining methods this will allow advanced understanding of part variations, providing insights into the next generation of engineering design and product management software.
This project is funded by Rolls-Royce plc as part of their support to the R-R University technology Centre for Computational Engineering at Southampton. In addition to the basic tax free student stipend of £15,141pa, R-R will provide a further tax free stipend increment of £9,000 pa and therefore in line with their graduate recruitment schemes. The stipend will rise in subsequent years. Funding for travel to international conferences will be available.
If you wish to discuss any details of the project informally, please contact Prof Andy Keane, Computational Engineering and Design Research Group, Email: email@example.com , Tel: +44 (0) 2380 59 2944.