Machine Learning for Computational Fluid Dynamics

We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2024. Applications are invited for the project title Machine Learning for Computational Fluid Dynamics

Successful applicants will receive an annual stipend (bursary) of £21,237, including inner London weighting, plus payment of their full-time home tuition fees for a period of 42 months (3.5 years).

You should be eligible for home (UK) tuition fees there are a very limited number (no more than three) of studentships available to overseas applicants, including EU nationals, who meet the academic entry criteria including English Language proficiency.

You will join the internationally recognised researchers in the Department of Mechanical Engineering research and PhD programmes | Brunel University London

The Project

As machine learning (ML) transforms our society, the next generation of engineering will also be revolutionised by these models. Computational fluid dynamics (CFD) solves equations to model fluid flow, to allow the design of faster cars and planes, optimise green technologies like wind turbines, enable biotechnology and make computer games and films more realistic. This project will look to apply transformers, the basic architecture of large language models (LLMs) like ChatGPT, to predict fluid dynamics in the same way they predict the next word in a sentence. This will be applied to the simplest example of turbulence, the minimal flow unit, incorporating molecular detail as part of a multi-physics simulation. This will be coupled with cutting-edge techniques like physics informed neural networks (PINNs) and super resolution from generative adversarial network (GANS) all run on a supercomputer with GPUs. During the project, the student will become an expert in machine learning, fluid dynamics and multi-physics simulation, while researching at the forefront of this exciting new field.

Please contact Dr Edward Smith at edward.smith@brunel.ac.uk for an informal discussion about the studentships.

Eligibility

Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science, Design, Mathematics, Physics, Chemistry or a similar discipline. A Postgraduate Masters degree is not required but may be an advantage.

Skills and Experience

Applicants will be required to demonstrate the following skills;

Strong grades in fluid dynamics and mathematics.
Experience in programming, ideally in Python but any high-level language (MATLAB, C++ or Fortran preferred).

You should be highly motivated, able to work independently as well as in a team, collaborate with others and have good communication skills.

How to apply

There are two stages of the application:

1.Applicants must submit the pre-application form via the following link

https://brunel.onlinesurveys.ac.uk/epsrc-dtp-24-25-pre-application-form-brunel-university-lon

by 16.00 on Friday 5th April 2024.

2.If you are shortlisted for the interview, you will be asked to email the following documentation in a single PDF file to cedps-studentships@brunel.ac.uk within 72hrs.

Your up-to-date CV;
Your Undergraduate degree certificate(s) and transcript(s) essential;
Your Postgraduate Masters degree certificate(s) and transcript(s) if applicable;
Your valid English Language qualification of IELTS 6.5 overall (minimum 6.0 in each section) or equivalent, if applicable;
Contact details for TWO referees, one of which can be an academic member of staff in the College.

Applicants should therefore ensure that they have all of this information in case they are shortlisted.

Interviews will take place in April/May 2024.

Meet the Supervisor(s)

Edward Smith - Edward Smith (www.edwardsmith.co.uk) is a researcher working on multi-scale methods combining particle and continuum simulation. He earned his PhD at Imperial College London, developing theoretical and computational techniques for the coupled simulation of molecular dynamics (MD) and computational fluid dynamics (CFD). After his PhD, he was awarded the post-doctoral excellence fellowship and published the first ever molecular dynamics simulation of near-wall turbulence. He spent time in Swinburne Australia working with experts in non-equilibrium molecular dynamics and statistical mechanics, before moving to Chemical Engineering at Imperial to work on multi-phase flow and the moving contact line. His next move was to Civil Engineering at Imperial to develop software (www.cpl-library.org), linking particles and continuum flows for granular systems. He recently took up a position at Brunel University London as a lecturer in fluid dynamics.