PyFR: Latest Developments and Future Roadmap - presented by Prof. Peter Vincent

PyFR: Latest Developments and Future Roadmap

Prof. Peter Vincent

Prof. Peter Vincent
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Associated Journal of Computational Physics article

N. A. Loppi et al. (2019) Locally adaptive pseudo-time stepping for high-order Flux Reconstruction. Journal of Computational Physics
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Article of record
PyFR: Latest Developments and Future Roadmap
Prof. Peter Vincent
Peter Vincent
Imperial College London

PyFR is a high-order accurate Computational Fluid Dynamics (CFD) solver based on the Flux Reconstruction approach of Huynh. It is written in Python, and can target a range of hardware platforms via an innovative Mako-based domain specific language. Over the past decade PyFR has matured into an established part of the open source CFD ecosystem. It now has an active community of users around the world, and is routinely applied to undertake high-fidelity scale-resolving simulations across a range of application areas, including at extreme scale. This talk will present results from some of the latest simulations to be undertaken with PyFR, and provide insight into some of our latest technological developments, including cache blocking optimisations for the CPU backend, and turbulence injection approaches. Finally, I will outline our roadmap for future development of the solver.

References
  • 1.
    S. Akkurt et al. (2021) Cache blocking strategies applied to flux reconstruction. Computer Physics Communications
  • 2.
    G. Giangaspero et al. (2021) Synthetic Turbulence Generation for High-Order Scale-Resolving Simulations on Unstructured Grids. AIAA Journal
  • 3.
    A. S. Iyer et al. (2021) High-order accurate direct numerical simulation of flow over a MTU-T161 low pressure turbine blade. Computers & Fluids
  • 4.
    F. D. Witherden and P. E. Vincent (2020) On nodal point sets for flux reconstruction. Journal of Computational and Applied Mathematics
  • 5.
    B. C. Vermeire et al. (2020) Optimal embedded pair Runge-Kutta schemes for pseudo-time stepping. Journal of Computational Physics
  • 6.
    N. A. Loppi et al. (2019) Locally adaptive pseudo-time stepping for high-order Flux Reconstruction. Journal of Computational Physics
  • 7.
    A. S. Iyer et al. (2019) Identifying eigenmodes of averaged small-amplitude perturbations to turbulent channel flow. Journal of Fluid Mechanics
  • 8.
    B. C. Vermeire et al. (2019) Optimal Runge–Kutta schemes for pseudo time-stepping with high-order unstructured methods. Journal of Computational Physics
  • 9.
    N. A. Loppi et al. (2018) A high-order cross-platform incompressible Navier–Stokes solver via artificial compressibility with application to a turbulent jet. Computer Physics Communications
  • 10.
    J. S. Park et al. (2017) High-Order Implicit Large-Eddy Simulations of Flow over a NACA0021 Aerofoil. AIAA Journal
  • 11.
    B. C. Vermeire et al. (2017) On the utility of GPU accelerated high-order methods for unsteady flow simulations: A comparison with industry-standard tools. Journal of Computational Physics
  • 12.
    B. C. Vermeire and P. E. Vincent (2016) On the behaviour of fully-discrete flux reconstruction schemes. Computer Methods in Applied Mechanics and Engineering
  • 13.
    B. C. Vermeire and P. E. Vincent (2016) On the properties of energy stable flux reconstruction schemes for implicit large eddy simulation. Journal of Computational Physics
  • 14.
    B. D. Wozniak et al. (2016) GiMMiK—Generating bespoke matrix multiplication kernels for accelerators: Application to high-order Computational Fluid Dynamics. Computer Physics Communications
  • 15.
    P. E. Vincent et al. (2015) An extended range of stable-symmetric-conservative Flux Reconstruction correction functions. Computer Methods in Applied Mechanics and Engineering
  • 16.
    F. D. Witherden et al. (2015) Heterogeneous computing on mixed unstructured grids with PyFR. Computers & Fluids
  • 17.
    F. D. Witherden and P. E. Vincent (2015) On the identification of symmetric quadrature rules for finite element methods. Computers & Mathematics with Applications
  • 18.
    F. D. Witherden et al. (2014) PyFR: An open source framework for solving advection–diffusion type problems on streaming architectures using the flux reconstruction approach. Computer Physics Communications
  • 19.
    F. D. Witherden and P. E. Vincent (2014) An Analysis of Solution Point Coordinates for Flux Reconstruction Schemes on Triangular Elements. Journal of Scientific Computing
  • 20.
    P. Castonguay et al. (2011) A New Class of High-Order Energy Stable Flux Reconstruction Schemes for Triangular Elements. Journal of Scientific Computing
  • 21.
    A. Jameson et al. (2011) On the Non-linear Stability of Flux Reconstruction Schemes. Journal of Scientific Computing
  • 22.
    P. E. Vincent and A. Jameson (2011) Facilitating the Adoption of Unstructured High-Order Methods Amongst a Wider Community of Fluid Dynamicists. Mathematical Modelling of Natural Phenomena
  • 23.
    P. E. Vincent et al. (2011) Insights from von Neumann analysis of high-order flux reconstruction schemes. Journal of Computational Physics
  • 24.
    P. E. Vincent et al. (2010) A New Class of High-Order Energy Stable Flux Reconstruction Schemes. Journal of Scientific Computing
  • 25.
    (2021) PyFR: Current Capabilities and Future Roadmap.
Grants
    Engineering and Physical Sciences Research CouncilEP/R029423/1Engineering and Physical Sciences Research CouncilEP/L000407/1Engineering and Physical Sciences Research CouncilEP/K027379/1Engineering and Physical Sciences Research CouncilEP/R030340/1European Commission635962Leverhulme TrustPhilip Leverhulme Prize