The archive comprises snapshot, point-probe, and time-average data produced via a high-fidelity computational simulation of turbulent air flow over a low pressure turbine blade, which is an important component in a jet engine. The simulation was undertaken using the open source PyFR flow solver on over 5000 Nvidia K20X GPUs of the Titan supercomputer at Oak Ridge National Laboratory under an INCITE award from the US DOE. The data can be used to develop an enhanced understanding of the complex three-dimensional unsteady air flow patterns over turbine blades in jet engines. This could in turn lead to design of greener more fuel efficient aircraft. It could also be used to train a next-generation of Reynolds Averaged Navier-Stokes turbulence models via a machine learning approach, which would have broad applicability to a wide range of science and engineering problems.
The .pyfrm mesh file for the simulation is:
The .ini configuration file for the simulation is:
The .pyfrs solution snapshot files produced by the simulation are:
The .csv point-probe data files produced by the simulation are:
The .pyfrs time-average files produced by the simulation are:
A given .pyfrs solution snapshot or time average file can be combined with the .pyfrm mesh file to create an associated .vtu or .pvtu file using the pyfr export command as detailed in the PyFR Documentation.
Once converted to a .vtu or .pvtu file the data can be interrogated programatically using e.g. the VTK Library or visualised and analysed directly using e.g. Paraview.
Details of the simulation setup and analysis of the results are available in the following paper: