180 research outputs found
Direct numerical simulation of turbulent counterflow nonpremixed flames
This paper presents our recent progress in terascale three-dimensional simulations of turbulent nonpremixed flames in the presence of a mean flow strain and fine water droplets. Under the ongoing university collaborative project supported by the DOE SciDAC Program [1] along with the INCITE 2007 Project [2], the study aims at bringing the state-of-the-art high-fidelity simulation capability to the next level by incorporating various advanced physical models for soot formation, radiative heat transfer, and lagrangian spray dynamics, to an unprecedented degree of detail in high-fidelity simulation application. The targeted science issue is fundamental characteristics of flame suppression by the complex interaction between turbulence, chemistry, radiation, and water spray. The high quality simulation data with full consideration of multi-physics processes will allow fundamental understanding of the key physical and chemical mechanisms in the flame quenching behavior. In this paper, recent efforts on numerical algorithms and model development toward the targeted terascale 3D simulations are discussed and some preliminary results are presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58180/2/jpconf7_78_012029.pd
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Visualization-driven Structural and Statistical Analysis of Turbulent Flows
Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In- teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro- vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa- cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul- tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool called VAPOR, which is in routine use by scientists internationally. In data from a 10243 simulation of a forced turbulent flow, VAPOR allowed us to perform a visual data exploration of the flow properties at interac- tive speeds, leading to the discovery of novel scientific properties of the flow, in the form of two distinct vortical structure populations. These structures would have been very difficult (if not impossible) to find with statistical overviews or other existing visualization-driven analysis ap- proaches. This kind of intelligent, focused analysis/refinement approach will become even more important as computational science moves to- wards petascale applications
I-Light Symposium 2005 Proceedings
I-Light was made possible by a special appropriation by the State of Indiana.
The research described at the I-Light Symposium has been supported by numerous grants from several sources.
Any opinions, findings and conclusions, or recommendations expressed in the 2005 I-Light Symposium Proceedings are those of the researchers and authors and do not necessarily reflect the views of the granting agencies.Indiana University Office of the Vice
President for Research and Information Technology, Purdue University Office of the
Vice President for Information Technology and CI
Combining in-situ and in-transit processing to enable extreme-scale scientific analysis
pre-printWith the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight
Extensible Terascale Facility (ETF): Indiana-Purdue Grid (IP-Grid)
NSF Award ID: ACI-0338618
Project Dates: 10/1/03-9/30/0
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