This thesis aims to automatically and reliably detect large-scale structures in turbulent flows. To achieve this, a U-net (a type of neural network) is trained using artificially generated data. From the network output, continuous structures are derived and general statistics, in
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This thesis aims to automatically and reliably detect large-scale structures in turbulent flows. To achieve this, a U-net (a type of neural network) is trained using artificially generated data. From the network output, continuous structures are derived and general statistics, including, volume fraction, relative kinetic energy and length scales (using PCA) are computed.
Detections were done on two homogenous isotropic turbulence (HIT) direct numerical simulation (DNS) datasets with Taylor Reynolds numbers of 175 and 1131, respectively. For both cases, the detected structures contained most of the volume and kinetic energy in the domain and were of the integral length scale. However, for the high Reynolds number, there were relatively half as many structures and the structures were roughly 4 times larger compared to those found in the low Reynolds number case.
