Start Date: Earliest start date is 1 August 2025 and latest start date is 1 July 2026
Introduction: Turbulent flows play a critical role in a wide range of natural and industrial processes, from weather systems and ocean currents to engineering applications such as aerodynamics, energy generation, and chemical processes. Understanding and accurately predicting turbulent behavior is key to optimising designs and improving efficiency. However, due to the inherently chaotic nature of turbulence and the wide range of spatial and temporal scales involved, high-fidelity simulations of turbulent flows on supercomputers remains a grand challenge, as they require immense computational power. Recent advances in heterogeneous computing systems, which integrate multi-core CPUs, GPUs, FPGAs, and specialised accelerators, offer the potential to dramatically improve the efficiency of turbulent flow simulations. However, effectively leveraging these architectures requires innovative algorithms and strategies tailored to exploit the specific strengths of different computational units.
The proposed research project will explore the potential of state-of-the-art computer architectures to simulate high-fidelity of turbulent flows. It will be based on Xcompact3d, an open-source finite-difference framework of fluid flow solvers dedicated to the study of turbulence.
Objectives: The objectives of the project are:
- To develop high-performance algorithms for high-fidelity simulations of turbulent flows that can efficiently leverage heterogeneous architectures.
- To validate and benchmark these algorithms on well-known turbulence problems, using a wide range of hardware.
- To apply the developed methodologies to the study of turbulent wake-to-wake interactions in large-scale wind farm during operations.
Supervisor: Professor Sylvain Laizet, expert in computational fluid dynamics and high performance computing: www.turbulencesimulation.com
Learning opportunities: You will develop knowledge and expertise in high performance computing, software development, programming, computational fluid dynamics and turbulence.
Professional Development: You will have access to engaging professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career Researcher Institute.
Duration: 3.5 years.
Funding: Full coverage of tuition fees and an annual tax-free stipend of £21,237 for Home, EU and International students. Information on fee status can be found on our fees and funding webpages.
Eligibility: Applicants should have a keen interest and solid background in computational fluid dynamics, programming and/or in high performance computing. You must possess (or expect to gain) a First class honours MEng/MSci degree or Distinction in a Master’s level degree in a relevant scientific or technical discipline.
How to apply: Submit your application via our Apply webpages. You will need to include the reference (AE0060) and address your application to Department of Aeronautics. When making your application, please type ‘Aeronautics Research (PhD)’ into the programme search bar.
For queries regarding the application process, please contact Lisa Kelly at: l.kelly@imperial.ac.uk
Application deadline: 9 January 2025
For further information: you can email Sylvain Laizet, Professor in Computational Fluid Mechanics: s.laizet@imperial.ac.uk
Equality, Diversity and Inclusion: Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.
PhD Contacts
PhD Administrator (Admissions)
Ms Lisa Kelly
l.kelly@imperial.ac.uk
PhD Administrator (On-course)
Ms Clodagh Li
c.li@imperial.ac.uk
Director of Postgraduate Studies (PhD)
Dr Chris Cantwell
c.cantwell@imperial.ac.uk
Senior Tutor for Postgraduate Research
Prof Joaquim Peiro
j.peiro@imperial.ac.uk
PhD Reps
Charlie Aveline (ca1119@ic.ac.uk)
Toby Bryce-Smith (tb1416@ic.ac.uk)
Katya Goodwin (yg7118@ic.ac.uk)
Paulina Gordina (pg919@ic.ac.uk)