About the project
This project aims to develop highly accurate and stable computational methodology to numerically solve Navier-Stokes equations and showcase its efficacy by applying it to a challenging problem of turbomachinery aeroacoustics in partnership with Rolls-Royce plc.
At the forefront of research on aerodynamics and aeroacoustics, we need highly accurate and reliable Computational Fluid Dynamics (CFD) methodology to numerically solve Navier-Stokes equations. Conventional CFD tools are, however, based on low-order discretisation which provides a good level of numerical stability but requires a very large number of grid points to compensate low resolution. As a consequence, higher-order discretisation has become popular in academia since it is substantially more efficient and accurate. The main issue with the high-order approaches so far is that their numerical stability is so poor that they are not widely applicable, particularly when complex geometry is involved. Also, almost all of them accompany cumbersome lower-order boundary treatment which eventually degrades the overall order of accuracy. In order to fix this issue and create a genuinely high-order discretisation framework, the lead supervisor of the project (Professor Kim) has done some foundation work.
In this project, we aim to complete the work using the latest Machine Learning technology to search and achieve the maximum level of stability and genuinely high-order discretisation framework. The new discretisation framework will be incorporated into CANARD (Compressible Aerodynamics & Aeroacoustics Research coDe) developed by Professor Kim at the University of Southampton, and its ultimate performance will be showcased by applying it to a challenging problem of turbomachinery aeroacoustics in partnership with Rolls-Royce plc. The computational simulations will be performed in massively parallel supercomputers (UK national supercomputer CIRRUS at the University of Edinburgh and a local IRIDIS-7 at the University of Southampton).
Training for the main code (CANARD) and massively parallel supercomputing will be provided in the initial stage. Also,You'll have opportunities to attend taught courses and external workshops to develop other essential skills including Machine Learning and Neural Network.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
