We develop novel numerical methods and apply them to solve challenging fluid flow problems in various areas of science, engineering, and medicine. We are particularly interested in theoretical aspects of high-order numerical methods for unstructured grids, as well as their implementation for a range of modern hardware platforms.


'Turbulent Channel Flow' - Checkout our latest paper on identifying eigenmodes of averaged small-amplitude perturbations to turbulent channel flow

'Step Inside a Jet Engine' - Results from our latest PyFR simulations of flow over low pressure turbine blades on show at the Imperial Fringe

'Implant may Offer Kidney Patients Easier Dialysis' - Our latest work on suppressing unsteady flow in arterio-venous fistulae featured in the Times

'New Symmetric Quadrature Rules' - Checkout our latest paper on identification of symmetric quadrature rules for finite element methods


Recent Papers

Identifying Eigenmodes of Averaged Small-Amplitude Perturbations to Turbulent Channel Flow. A. S. Iyer, F. D. Witherden, S. I. Chernyshenko, P. E. Vincent. Journal of Fluid Mechanics, Volume 875, Pages 758-780, 2019.
Abstract: Eigenmodes of averaged small-amplitude perturbations to a turbulent channel flow - which is one of the most fundamental canonical flows - are identified for the first time via an extensive set of high-fidelity GPU-accelerated direct numerical simulations. While the system governing averaged small-amplitude perturbations to turbulent channel flow remains unknown, the fact such eigenmodes can be identified constitutes direct evidence that it is linear. Moreover, while the eigenvalue associated with the slowest-decaying anti-symmetric eigenmode mode is found to be real, the eigenvalue associated with the slowest-decaying symmetric eigenmode mode is found to be complex. This indicates that the unknown linear system governing the evolution of averaged small-amplitude perturbations cannot be self-adjoint, even for the case of a uni-directional flow. In addition to elucidating aspects of the flow physics, the findings provide guidance for development of new unsteady Reynolds-averaged Navier-Stokes turbulence models, and constitute a new and accessible benchmark problem for assessing the performance of existing models, which are used widely throughout industry.

Optimization of 3D Divergence Free Flow Field Reconstruction Using 2D Ultrasound Vector Flow Imaging. X. Zhou, P. E. Vincent, X. Zhou, C. H. Leow, M. X. Tang. Accepted for publication in Ultrasound in Medicine and Biology.
Abstract: 3-D blood vector flow imaging is of great value in understanding and detecting cardiovascular diseases. Currently, 3-D ultrasound vector flow imaging requires 2-D matrix probes, which are expensive and suffer from suboptimal image quality. Our recent study proposed an interpolation algorithm to obtain a divergence-free reconstruction of the 3-D flow field from 2-D velocities obtained by high-frame-rate ultrasound particle imaging velocimetry (HFR echo-PIV, also known as HFR UIV), using a 1-D array transducer. The aim of this work was to significantly improve the accuracy and reduce the time-to-solution of our previous approach, thereby paving the way for clinical translation. More specifically, accuracy was improved by optimising the divergence-free basis to reduce Runge phenomena near domain boundaries, and time-to-solution was reduced by demonstrating that under certain conditions, the resulting system could be solved using widely available and highly optimised generalised minimum residual algorithms. To initially illustrate the utility of the approach, coarse 2-D subsamplings of an analytical unsteady Womersely flow solution and a steady helical flow solution obtained using computational fluid dynamics were used successfully to reconstruct full flow solutions, with 0.82% and 4.8% average relative errors in the velocity field, respectively. Subsequently, multiplane 2-D velocity fields were obtained through HFR UIV for a straight-tube phantom and a carotid bifurcation phantom, from which full 3-D flow fields were reconstructed. These were then compared with flow fields obtained via computational fluid dynamics in each of the two configurations, and average relative errors of 6.01% and 12.8% in the velocity field were obtained. These results reflect 15%-75% improvements in accuracy and 53- to 874-fold acceleration of reconstruction speeds for the four cases, compared with the previous divergence-free flow reconstruction method. In conclusion, the proposed method provides an effective and fast method to reconstruct 3-D 80 flow in arteries using a 1-D array transducer.



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).


Recent Seminars

Towards Green Aviation with Python at Petascale. P. E. Vincent. Tokyo University of Science, Tokyo, Japan. December 2017.
Next Generation CFD: High-Order Accurate Simulations using Many-Core Platforms. P. E. Vincent. Swiss National Supercomputing Center, Lugano, Switzerland. August 2016.