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

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.

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

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.

To apply, please contact the main supervisor; Prof Andrew Masters - andrew.masters@manchester.ac.uk. 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.

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