Flux Reconstruction


High-order numerical methods for unstructured grids combine the superior accuracy of high-order spectral or finite difference methods with the geometrical flexibility of low-order finite volume or finite element schemes. The Flux Reconstruction (FR) approach unifies various high-order schemes for unstructured grids within a single framework. Additionally, the FR approach exhibits a significant degree of element locality, and is thus able to run efficiently on modern streaming architectures, such as Graphical Processing Units (GPUs). The aforementioned properties of FR mean it offers a promising route to performing affordable, and hence industrially relevant, scale-resolving simulations of hitherto intractable unsteady flows (involving separation, acoustics etc.) within the vicinity of real-world engineering geometries. An detailed overview of the FR approach is given in:

Linear Stability

The linear stability of an FR schemes depends on the form of the correction function. Linear stability issues are discussed in:

Latest Release

PyFR 1.6.0:

  • Added incompressible Euler and Navier-Stokes solvers.
  • Added support for Intel KNL.
  • Added libxssm as a matrix multiplication provider for C/OpenMP backend.
  • Added kernel caching for C/OpenMP backend.

Join our Team

Postdoctoral Position - GPU Accelerated High-Order Computational Fluid Dynamics
Summary: A fully funded Postdoctoral position is currently available. The project, will involve development of PyFR, an open-source high-order massively-parallel cross-platform CFD solver, as well as its application to solve a range of challenging unsteady flow problems. Candidates should hold, or expect to obtain, a PhD in a numerate discipline from a world-leading university.