Accurate simulation of unsteady turbulent
flow is critical for improved design of greener aircraft
that are quieter and more fuel-efficient. We demonstrate
application of PyFR, a Python based computational fluid
dynamics solver, to petascale simulation of such flow
problems. Rationale behind algorithmic choices, which
offer increased levels of accuracy and enable sustained
computation at up to 58% of peak DP-FLOP/s on unstruc-
tured grids, will be discussed in the context of modern
hardware. A range of software innovations will also be
detailed, including use of runtime code generation, which
enables PyFR to efficiently target multiple platforms,
including heterogeneous systems, via a single implemen-
tation. Finally, results will be presented from a full-
scale simulation of flow over a low-pressure turbine blade
cascade, along with weak/strong scaling statistics from the
Piz Daint and Titan supercomputers, and performance
data demonstrating sustained computation at up to 13.7
DP-PFLOP/s.