Updates to a public turbulence database system and applications to studying local features of the energy cascade

We describe updates to an open big-data system that houses large datasets from direct numerical simulations of fluid turbulence. The JHTDB (Johns Hopkins Turbulence Databases) has been operating for over a decade and has led to hundreds of peer-reviewed articles on turbulence. A new set of analysis tools based on Jupyter notebooks has been developed that enable direct access to subsets of the data based on the virtual sensors concept. These notebooks provide fast and stable operation on the existing turbulence data sets while enabling user-programmable, server-side computations. To date, the new data access tools have been tested mostly on the high Reynolds number, forced isotropic turbulence data set at a Taylor microscope Reynolds number of 1,300. We report on a novel analysis based on the Karman-Howarth-Monin-Hill (KHMH) equation, a generalization of the Karman-Howarth equation relating third-order velocity increment moments to the rate of viscous dissipation in the flow and separation length-scale. We explore various implications of Kolmogorov's refined similarity hypothesis (KRSH) that states that statistics in the inertial range conditioned on the locally averaged dissipation rate at that scale are universal. Conditional statistics of local third-order structure function evaluated from the dataset show good agreement with KRSH predictions. The ability to access local regions in vast amounts of turbulence data without the need to download entire datasets and the ability to perform the analysis near where the data reside have greatly enhanced the flexibility needed for the sort of exploratory analysis and results to be presented