Data exploration of turbulence simulations using a database cluster

By E. Perlman, Randal C. Burns, Yi Li, Charles Meneveau. International Conference on Software Composition, 2007

348 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/972c5cddbb79647dd9299f24f635d54825ee355a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Barnab'as P'oczos
2. Liang Xiong
3. Jeff Schneider
4. Bernd R. Noack
5. Randal C. Burns
6. Steven L. Brunton

In Information Systems and Management

1. Barnab'as P'oczos
2. Jeff Schneider
3. Liang Xiong

Citations in the corpus

• NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations (2020, methodology): “In the current study, we address the aforementioned two questions system-3 atically by using analytical solutions for two-dimensional and three-dimensional flows and also by comparing with DNS of turbulent channel flow available at [20, 21, 22].”
• Machine Learning for Fluid Mechanics (2019, background): “However, the analysis of fluid mechanics data has relied ,to a large extent, on domain expertise, statistical analysis, and heuristic algorithms.”
• Support Distribution Machines (2012, methodology): “We performed an exploratory experiment on using SDM classifiers to assist in this process, using turbulence data from the JHU Turbulence Data Cluster 2 (Perlman et al., 2007) (TDC).”
• Proposal : Scalable , Active and Flexible Learning on Distributions (2015, neutral): “To illustrate this setting, we consider the results of a large-scale simulation of a turbulent fluid in three dimensions over time in the Johns Hopkins Turbulence Databases4 (Perlman et al. 2007). Following Sutherland, Xiong, et al. (2012), we aim to recognize vortices in two-dimensional slices of t”
• Group Anomaly Detection using Flexible Genre Models (2011, methodology): “In each time step and each 2http://turbulence.pha.jhu.edu vertex of the grid, TDC records the 3-dimensional velocity of the fluid.”
• Deep learning methods for super-resolution reconstruction of turbulent flows (2020, background): “Johns Hopkins turbulence databases 34,35 (JHTDB) are open-source simulation databases containing space-time histories of direct numerical simulations of several classical turbulent flow problems.”
• Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “…to the development of data-driven learning algorithms (e.g., deep learning (LeCun et al. 2015)), computational equipment (e.g., graphical process units), big data (e.g., Johns– Hopkins Turbulence Database (JHTDB) (Perlman et al. 2007)), and open-source code (e.g., TensorFlow (Abadi et al. 2015)).”
• A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence (2008, methodology): “We have found that using an atom of size 643 data points as the fundamental I/O unit is the most efficient because it balances the memory and disk performance [9] in our application.”