In this presentation, we describe a turbulence database system, together with a sample application to a transitional boundary layer flow focusing on scaling properties of turbulent spots during boundary layer transition. The Johns Hopkins Turbulence Databases (JHTDB) exposes large-scale turbulent data to the research community while at the same time providing easy-to-use client interfaces based on Web Services that act as “virtual flow sensors” that can be placed in the turbulent flows. This approach has greatly facilitated retrieving and interacting with the data.

At present JHTDB contains over 1/2 Petabyte of data from various turbulent flow simulations. The data have been used in over 200 peer-reviewed journal publications on turbulence from authors world-wide. We also present an application of the transitional (by-pass) boundary layer dataset contained in JHTDB. Specifically, we develop a new approach to detect the interface that separates the turbulent boundary layer from the laminar or outer regions of the flow using machine learning. A self-organized map based clustering method is shown to enable determination of the interface without having to prescribe arbitrary threshold values as traditional interface detection methods require. Scaling properties of the interface are studied and links to fractal properties of turbulent non-turbulent interfaces in high Reynolds number flows are established. This work has been performed with Drs. Zhao Wu and Tamer Zaki, while the database (supported by the NSF) has resulted from a long-term collaboration with the JHTDB team.

Speaker Biography: 

Charles Meneveau is the Louis M. Sardella Professor in the Department of Mechanical Engineering and is Associate Director of the Institute for Data Intensive Engineering and Science (IDIES).  His area of research is focused on understanding and modeling hydrodynamic turbulence, and complexity in fluid mechanics in general. The insights that have emerged from Professor Meneveau’s work have led to new numerical models for Large Eddy Simulations (LES) and applications in engineering and environmental flows, including wind farms. He also focuses on developing methods to share the very large data sets that arise in computational fluid dynamics.

Meneveau received his B.S. degree in Mechanical Engineering from the Universidad Técnica Federico Santa María in Valparaíso, Chile, in 1985 and advanced degrees from Yale University. He then was a postdoctoral fellow at the Center for Turbulence Research at Stanford and has been on the Johns Hopkins University faculty since 1990. Prof. Meneveau is Deputy Editor of the Journal of Fluid Mechanics and has served as the Editor-in-Chief of the Journal of Turbulence. Professor Meneveau is a member of the US National Academy of Engineering, a foreign corresponding member of the Chilean Academy of Sciences, and a Fellow of APS and ASME.
 

Zoom Link: https://argonne.zoomgov.com/j/1619902756pwd=cEpWUU1DbUU1OGhKbmdObzZIdEMvZz09