Supervisor: Dr Andy Higginbotham (University of York)
Inertial confinement fusion (ICF) is one of a number of research fields which rely on the dynamic compression of materials. These approaches compress materials on a timescale comparable to or exceeding the sound speed transit time, and as such, require material to respond at the limit of its ability. Such responses are, by their very nature, highly rate dependent, and require a detailed understanding of microstructural atomic arrangement and rearrangement to explain.
Despite the atomistic level of the governing physics, computational study of dynamic compression pathways in real systems is nearly always carried out in hydrodynamic codes which abstract away this information into equations of state; analytic or tabular models of the links between intrinsic thermodynamic variables such as density, pressure and temperature. The more complex of these models can include rate dependence, or analytic terms modelling microscopic processes, but they are all ultimately lacking in real microscopic insight.
Some work, including that carried out in York, uses molecular dynamics (an atomistic method) to study dynamic compression on its natural length scale. This allows for a potentially more faithful reproduction of the underlying physics, and better insight into material response. However, such calculations are computationally expensive, and thus limited to samples of order a micron in size, far too small to model full samples for ICF or other materials studies utilising dynamic methods.
To tackle this mismatch, recent work in York has developed a hybrid hydrocode/atomistic approach which uses machine learning to couple the results of small-scale atomistic simulations with a specifically written hydrocode model. This approach effectively replaces the hydrocode’s equation of state with an atomistically informed model of arbitrary complexity. We have been able to demonstrate fidelity between atomistic and hydrocode approaches to better than 10% with computational speed up of over 1000 times compared to fully atomistic simulation. Such an approach, if expanded, would open avenues for modelling and interpreting experiments with previously unattainable accuracy.
Current work has focussed on one specific problem: compression of an elastoplastic solid to modest pressures. This project will aim to explore the limits of the approach, expanding to higher pressures, and a more diverse range of materials. This could include the warm dense conditions found in ICF plasmas and stars or pushing to high pressure solids by replacing the current, classical atomistic simulations with more advanced ab-initio approaches.
This project is primarily computational in scope, and will require the student to engage in model development, programming in C++ and python, and machine learning. A familiarity with some of these would be beneficial, but training will be provided for any skills you have not yet had chance to develop. There is also the potential for the student to take part in experiments if they want to expand their expertise and knowledge beyond the computational.
The project will be based in York, but you will have chance to collaborate with colleagues both within the UK and internationally, as well as at international x-ray facilities.
This project is offered by University of York. For further information please contact: Dr Andy Higginbotham (firstname.lastname@example.org).
This project may be compatible with part time study, please contact the project supervisors if you are interested in exploring this.