University of Surrey Featured PhD Programmes
University of Bristol Featured PhD Programmes
University of Leeds Featured PhD Programmes

Robust Validation Methods for Computational Fluid Dynamics Models of Waste Flows

   School of Engineering

Liverpool United Kingdom Mechanical Engineering

About the Project

Establishing credibility in engineering simulations through a process of model validation is an essential part of any engineering analysis [Patterson, 2015]. It is well-established that validation of computational models should include a comparison of predictions from the simulation with measurements from a physical experiment or prototype that closely resembles the conditions in service, i.e. real-world, everyday circumstances. Advances in digital sensor technology allow information-rich data fields to be acquire in real-time leading to large quantities of measured data, sometimes referred to as ‘big data’, which presents challenges in making quantitative and meaningful comparisons with predictions from simulations. In solid mechanics, Patterson and his co-workers have used orthogonal decomposition to reduce the dimensionality of both measured and predicted data to feature vectors that can readily be compared [Dvurecenska et al, 2018]. This process is becoming routine for two-dimensional fields of data and is being developed for volumes of data. In both cases, feature vectors representing measurements and predictions can be compared to establish the probability that they belong to the same population. In this project these techniques will be extended to fluid dynamics in collaboration with Professor Rob Poole. It is expected that the outcome will be a significant advance in the rigour with which simulations based on computational fluid dynamics models can be validated through detailed comparison to measurements and that this will enable updating of simulations to increase their fidelity. 

For more on our research on model credibility see

The project is sponsored by EPSRC through the GREEN CDT and the National Nuclear Laboratory. Dr Steve Graham from NNL will be the industrial supervisor and will provide guidance on the industrial usage of the technology and its application to a case study on waste flows. There will cohort-based training in year 1 provided by the CDT that will include taught modules on nuclear science and engineering and specialised training tailored to this PhD project which might include a placement at NNL.

Applicant Eligibility

Candidates will have, or are due to obtain, a Master’s Degree or equivalent from a reputable University in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Application Process

Candidates wishing to apply should complete the University of Liverpool application form accessed via the blue 'Apply online' button here: applying for a PhD in Engineering uploading: Degree Certificates & Transcripts, an up to date CV, a covering letter/personal statement and two references. If a candidate wishes to apply for more than one project, should also upload a document listing the preferred projects in a ranked order. 


Candidates wishing to discuss the research project should contact the primary supervisor, Professor Eann Patterson [], those wishing to discuss the application process should discuss this with the School Postgraduate Office [].

Funding Notes

This studentship is for UK [home] students only and has a financial package including: annual stipend at the UKRI rate [currently £15,285 per annum], student fees and a research support grant [for conferences & travel, consumables etc] for 4 academic years.


Patterson EA, On the credibility of engineering models and meta-models, J. Strain Analysis,, 2015.
Dvurecenska K, Graham S, Patelli E, Patterson EA. A probabilistic metric for the validation of computational models. Royal Society open science. 14;5(11):180687, 2018.

Email Now

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.