Research

Numerical Methods (Theory)

High-Order Methods For Unstructured Grids
Summary: We are interested in various theoretical aspects of high-order numerical methods for unstructured grids. In particular, we are interested in development of so called 'Flux Reconstruction' methods, and associated methodologies for high-order simulation of unsteady turbulent compressible flows in the vicinity of complex geometries.

Numerical Methods (Implementation)

Massively-Parallel Computing
Summary: We are interested in developing software that can efficiently target massively-parallel hardware, such as clusters of Nvidia Tesla GPUs.

Hardware Independent Coding Paradigms
Summary: We are interested in developing software that can simultaneously target a range of hardware platforms, including heterogeneous systems, from a single codebase.

Numerical Methods (Application)

Compressible Aerodynamics
Summary: We are interested in application of computational tools to solve hitherto intractable compressible flow problems within the vicinity of complex engineering geometries. We are particularly interested in compressible flow problems associated with the design of next generation unmanned aerial vehicles.

Biological Fluid Dynamics
Summary: We are interested in simulating blood flow within various regions of the vasculature. We are particularly interested in simulating flow within arterio-venous fistulae (artificial vascular junctions formed in the wrists of patients who need dialysis). Our objective is to understand whether 'abnormal' flow patterns within these fistulae cause them to block and fail.

Openings

PhD Studentship - Development of In-situ Visualisation and Analysis Technology for High-Fidelity Computational Fluid Dynamics
Summary: A PhD Studentship is currently available. The project, will involve addition of 'in-situ' visualisation, processing, and analysis technology to PyFR, an open-source high-order massively-parallel CFD platform, as well as its application to solve a range of challenging unsteady flow problems. Candidates should hold, or expect to obtain, an undergraduate degree in a numerate discipline. Previous programming experience is important (ideally Python, C++ and CUDA).

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