F
Freddie D. Witherden
Researcher at Texas A&M University
Publications - 82
Citations - 1580
Freddie D. Witherden is an academic researcher from Texas A&M University. The author has contributed to research in topics: Computational fluid dynamics & Reynolds number. The author has an hindex of 17, co-authored 74 publications receiving 1000 citations. Previous affiliations of Freddie D. Witherden include Stanford University & Imperial College London.
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Journal ArticleDOI
PyFR: An open source framework for solving advection–diffusion type problems on streaming architectures using the flux reconstruction approach
TL;DR: The Flux Reconstruction (FR) approach unifies various high-order schemes for unstructured grids within a single framework, and is thus able to run efficiently on modern streaming architectures, such as Graphical Processing Units (GPUs).
Journal ArticleDOI
On the utility of GPU accelerated high-order methods for unsteady flow simulations
TL;DR: This study systematically compares accuracy and cost of the high-order Flux Reconstruction solver PyFR running on GPUs and the industry-standard solver STAR-CCM+ running on CPUs when applied to a range of unsteady flow problems.
Proceedings Article
Deep Dynamical Modeling and Control of Unsteady Fluid Flows
TL;DR: The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons and is able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinders.
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On the identification of symmetric quadrature rules for finite element methods
TL;DR: A methodology for the identification of symmetric quadrature rules inside of quadrilaterals, triangles, tetrahedra, prisms, pyramids, and hexahedra is described and many of the rules appear to be new, and an improvement over those tabulated in the literature.
Proceedings ArticleDOI
Towards green aviation with python at petascale
TL;DR: Application of PyFR, a Python based computational fluid dynamics solver, to petascale simulation of unsteady turbulent flow is demonstrated, and a range of software innovations will be detailed, including use of runtime code generation, which enables PyFR to efficiently target multiple platforms, including heterogeneous systems, via a single implementation.