Scalar mixing in direct numerical simulations of temporally evolving plane jet flames with skeletal CO/H2 kinetics

Hawkes, Evatt R.; Sankaran, Ramanan; Sutherland, James C.; Chen, Jacqueline H.

doi:10.1016/j.proci.2006.08.079

Cited by 200 publications

(146 citation statements)

References 30 publications

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“…In the case study presented here the data describes a temporallyevolving turbulent CO/H 2 jet flame undergoing extinction and reignition at different Reynolds numbers [30]. The simulations were performed with up to 0.5 billion grid points and periodic boundary conditions in the mean flow (x) direction.…”

Section: Resultsmentioning

confidence: 99%

“…Reactive flows have been studied more recently [30,34] by directly computing and tracking χ within the simulation. However, measurements were limited to thickness only and did not explore the relationship between regions of high χ and temperature.…”

Section: Related Workmentioning

confidence: 99%

“…To make these assessments scientists use Direct Numerical Simulations (DNS) of turbulent flames [29], to study effects such as flame autoignition [19] and extinction [30]. One of the primary drivers of these phenomena is the rate of turbulent mixing, characterized locally by the scalar dissipation rate, χ. Compressive strain in directions aligned to scalar gradients, creates thin pancake-like regions in the simulation whose thickness provides a direct measure of the local mixing lengthscale and its scaling with turbulence length scales.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature-Based Statistical Analysis of Combustion Simulation Data

Bennett

Krishnamoorthy

Liu

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Our framework provides a natural and intuitive work-flow for the exploration of global trends in feature-based statistics. By efficiently encoding hierarchical meta-data in a pre-processing step, interactive data exploration of the equivalent of one terabyte of simulation data is performed on a commodity desktop.Abstract-We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-Based Statistical Analysis of Combustion Simulation Data

Bennett

Krishnamoorthy

Liu

et al. 2011

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…The massively parallel direct numerical simulation (DNS) solver (S3D) -developed at Sandia National Laboratories -solves the full compressible Navier-Stokes, total energy, species, and mass continuity equations coupled with detailed chemistry [17,18,19]. The application was run in SMP (one core per node) as well as VN mode (four cores per node) on Jaguar.…”

Section: Multi-core Performance Measurementmentioning

confidence: 99%

Collecting Performance Data with PAPI-C

Terpstra

Jagode²,

You³

et al. 2010

Tools for High Performance Computing 2009

250

105

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Modern high performance computer systems continue to increase in size and complexity. Tools to measure application performance in these increasingly complex environments must also increase the richness of their measurements to provide insights into the increasingly intricate ways in which software and hardware interact. PAPI (the Performance API) has provided consistent platform and operating system independent access to CPU hardware performance counters for nearly a decade. Recent trends toward massively parallel multi-core systems with often heterogeneous architectures present new challenges for the measurement of hardware performance information, which is now available not only on the CPU core itself, but scattered across the chip and system. We discuss the evolution of PAPI into Component PAPI, or PAPI-C, in which multiple sources of performance data can be measured simultaneously via a common software interface. Several examples of components and component data measurements are discussed. We explore the challenges to hardware performance measurement in existing multi-core architectures. We conclude with an exploration of future directions for the PAPI interface.

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“…This time scale approximation is likely to require modification due to the influence of chemical reaction on the time scale ratio. 13,14 However, extended models are currently at the tentative stage and the standard approach has been preferred. The drift and diffusion coefficients are…”

Section: ͑1͒mentioning

confidence: 99%

Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer

Wandel

Lindstedt

2009

Physics of Fluids

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A novel, stochastic, hybrid binomial Langevin-multiple mapping conditioning ͑MMC͒ model-that utilizes the strengths of each component-has been developed for inhomogeneous flows. The implementation has the advantage of naturally incorporating velocity-scalar interactions through the binomial Langevin model and using this joint probability density function ͑PDF͒ to define a reference variable for the MMC part of the model. The approach has the advantage that the difficulties encountered with the binomial Langevin model in modeling scalars with nonelementary bounds are removed. The formulation of the closure leads to locality in scalar space and permits the use of simple approaches ͑e.g., the modified Curl's model͒ for transport in the reference space. The overall closure was evaluated through application to a chemically reacting mixing layer. The results show encouraging comparisons with experimental data for the first two moments of the PDF and plausible results for higher moments at a relatively modest computational cost.

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Scalar mixing in direct numerical simulations of temporally evolving plane jet flames with skeletal CO/H2 kinetics

Cited by 200 publications

References 30 publications

Feature-Based Statistical Analysis of Combustion Simulation Data

Feature-Based Statistical Analysis of Combustion Simulation Data

Collecting Performance Data with PAPI-C

Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer

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