The phase behaviour and dynamics of lubricants: a new, coarse-grained simulation approach

Hypothesis and Objectives

Novel coarse-grained computational simulations can predict the phase behaviour and rheology of lubricants

• Develop new simulation methodologies to predict equilibrium and rheology of lubricants in oil.

• Use experimental data from Infineum Ltd. to refine models and develop design rules for more effective formulations

Project Description

Lubricants are not simple liquids. The surfactant molecules present in such systems self-assemble into aggregates and these aggregates, in turn, interact with each other to form a wide variety of phases. These structures have a profound effect on a lubricant’s adsorption and rheology, both key factors affecting lubricant efficiency. Because of the complexity of the systems special modelling methods are required to probe this behaviour. This PhD project is aimed at developing a novel methodology based on the use of local density dependent potentials (LDPs).

Specific aims are:

• To apply the current methodology to lubricant systems, so as to model aggregate formation and surface adsorption. We aim for quantitative agreement with experimental data supplied by Infineum Ltd.

• To incorporate Machine Learning (ML) algorithms into the calculation of the forces on the constituent particles, so as to significantly speed up the algorithm.

• To incorporate the Smooth Particle Hydrodynamics equations of motion into the model, which will allow for the study of surfactant dynamics on a rheological time-scale.

The student will be trained in the use of high-performance computing and in programming. They will also receive a firm grounding in rheology, statistical mechanics and the use of Machine Learning techniques.

Impacts:

• Improved predictions of the properties of lubricant formulations, with a potential for reduction of CO2 emissions.

• Improved novel computation methodologies that can be applied over a wide range of other topics

• Incorporation of Machine Learning methodologies for enhanced computation efficiency.

Benefits for the student:

• Mastering two important theoretical methodologies (coarse-grained simulation and machine learning) and skills involved in using high-performance computing

• Continual interaction with an ongoing experimental program

• Collaboration with industry

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Funding

This 3.5 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will received an annual tax free stipend set at the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).