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Research Studentship in Impact Engineering

Research Studentship in Impact Engineering

Project: Modelling Phase Change in cryogenic multiphase flows using CFD and Machine Learning techniques

3.5-year DPhil studentship 

Supervisors: Dr Konstantina Vogiatzaki 

Cryogenic processes are the core technology of various applications, currently under development in order to tackle grand challenges in advanced healthcare (cryo-surgery is a less invasive -in comparison to other surgical procedures- treatment of cancer tumours), energy sustainability (cryogenic energy storage contributes to harness renewables for carbon-neutral refrigeration and power supply), space exploration (cryogenic O2 (LO2) and H2 (LH2) are used in rocket engines) and cooling (liquid nitrogen (LN2) and liquid helium (LHe) are used to cool superconducting magnets of MRI).

Until now new technological developments in the cryogenic field relied solely on experiments. However, performing lab experiments with all the possible combinations of fluids and conditions to determine the optimum operability manifold is very expensive and not always accurate, mainly because of the limitations of experimental techniques, safety issues relating to handling of cryogenic fluids, and lack of experimental equipment that can be operated in very low temperatures. Simulation tools have the potential to reduce the dependency on the experiments and increase our understanding of cryogenic physics in a cost and time effective manner. Unfortunately, regardless of the vast progress in terms of virtual design tools tailored for ultra-high temperature processes (combustion) in engines and gas turbines, robust numerical tools specifically designed for ultra-low temperature conditions do not currently exist.

This project aims to develop a novel computational framework combining CFD, Molecular Dynamics and ML techniques for the development of accurate and cost-effective modelling tools of the phase change processes taking place in ultra-low temperatures, starting from molecular scale information to describe large-scale process. This can help researchers and industry to tackle two of the major challenges in the field of phase transitions: 1) combining traditional methodologies with new data-driven approaches deriving robust macroscopic models based on molecular dynamics; 2) extending the applicability range that will allow calibration of the models to a wider range of conditions in a cost-effective manner.

This project is an excellent opportunity to undertake fundamental research in the field of multiphase flows, involving elements of the rapidly evolving field of Machine Learning.


This studentship is funded through the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership and is open to Home students (full award – home fees plus stipend). Full details of the eligibility requirements can be found on the UK Research and Innovation website.

There is very limited flexibility to support international students. If you are an international student and want to apply for this studentship please contact the supervisor to see whether the flexibility might be available for you.

Award Value

Course fees are covered at the level set for Home students (c. £8,965 p.a.). The stipend (tax-free maintenance grant) is c. £17,668 p.a. for the first year, and at least this amount for two and a half years.

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

A first class honours degree in Engineering, Physics or Computer Science
Excellent English written and spoken communication skills

The following skills are also highly desirable:

Programming experience (i.e. Matlab, Python,C++ etc.)
Strong interest in code development and high performance computing
Good Knowledge of one of the commercial CFD softwares (OpenFOAM, FLUENT, CONVERGE etc)
Ability to work within specific deadlines

Application Procedure

Informal enquiries are encouraged and should be addressed to Prof Konstantina Vogiatzaki (

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.

Please quote 23ENGMM_KV in all correspondence and in your graduate application.

Application deadline: noon on 9th December 2022

Start date: October 2023