A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
By Yi Li, E. Perlman, M. Wan, Yunke Yang, Charles Meneveau, Randal C. Burns, Shiyi Chen, A. Szalay, Gregory L. Eyink. 2008
770 citations recorded by the source corpus.
A public database system archiving a direct numerical simulation (DNS) data set of isotropic, forced turbulence is described in this paper. The data set consists of the DNS output on 10243 spatial points and 1024 time samples spanning about one large-scale turnover time. This complete 10244 spacetime history of turbulence is accessible to users remotely through an interface that is based on the Web-services model. Users may write and execute analysis programs on their host computers, while the programs make subroutine-like calls that request desired parts of the data over the network. The users are thus able to perform numerical experiments by accessing the 27 terabytes (TB) of DNS data using regular platforms such as laptops. The architecture of the database is explained, as are some of the locally defined functions, such as differentiation and interpolation. Test calculations are performed to illustrate the usage of the system and to verify the accuracy of the methods. The database is then used to analyze a dynamical model for small-scale intermittency in turbulence. Specifically, the dynamical effects of pressure and viscous terms on the Lagrangian evolution of velocity increments are evaluated using conditional averages calculated from the DNS data in the database. It is shown that these effects differ considerably among themselves and thus require different modeling strategies in Lagrangian models of velocity increments and intermittency.
Subfields: Atmospheric Science, Computational Mechanics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/da55154d84a433fa7dbf9b2a513d402447dd35c4
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In Atmospheric Science
- Shengze Cai
- Heng Xiao
- Gianluca Iaccarino
- G. Em Karniadakis
- Paola Cinnella
- Karthik Duraisamy
- Paris Perdikaris
- Jian-Xun Wang
In Computational Mechanics
- Heng Xiao
- Shengze Cai
- G. Em Karniadakis
- Paola Cinnella
- Karthik Duraisamy
- Hui Li
- Gianluca Iaccarino
- Xiaowei Jin
In Global and Planetary Change
Citations in the corpus
- Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, neutral): “Details are given in Perlman et al. (2007) and Li et al. (2008).”
- Turbulence Modeling in the Age of Data (2018, methodology): “There has been a concerted effort by the turbulence community to gather and archive data sets including the databases in US (Li et al. 2008) and Europe (Coupland 1993), among others.”
- 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].”
- 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.”
- Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “[163] first used Bayesian inference to calibrate the model coefficients Cμ and C based on a DNS database of forced isotropic turbulence in a periodic box [164].”