Turbulence-Flame Interaction

A fluid dynamics video was created using data from a Direct Numerical Simulation of a premixed n-heptane flame at high Karlovitz number. The magnitude of vorticity and progress variable(a monotonically increasing variable through the flame) illustrate the turbulence-flame interaction.Comment: Two videos are included for the 2013 Gallery of Fluid Motio

Effects of dissipation rate and diffusion rate of the progress variable on local fuel burning rate in premixed turbulent flames

The validity of the premixed flamelet equations and the dependence of the fuel burning rate on the parameters involved in these equations have been investigated using a large series of direct numerical simulations of turbulent premixed flames in the thin reaction zones (TRZ) and the distributed reaction zones (DRZ) regimes. Methane, toluene, n-heptane, and iso-octane fuels were considered over a wide range of unburnt conditions and turbulence characteristics. Flames with unity and non-unity Lewis numbers were investigated separately to isolate turbulence-chemistry interaction from differential diffusion effects. In both cases, the flamelet equations, which rely on the assumption of a thin reaction zone, are locally valid throughout the TRZ regime, more precisely up to a Karlovitz number at the reaction zone of 10 (based on the definition used in this paper). Consistent with this result, in the unity Lewis number limit, the fuel burning rate is strongly correlated with the dissipation rate of the progress variable, the only parameter in the flamelet equations. In the non-unity Lewis number case, the burning rate is a strong function of both the dissipation rate and the diffusion rate, both of which are parameters in the flamelet equations. In particular, the correlation with these parameters is significantly better than with curvature or tangential strain rate

Folding: reporting instantaneous performance metrics and source-code references

Despite supercomputers deliver huge computational power, applications only reach a fraction of it. There are several factors limiting the application performance, and one of the most important is the single processor efficiency because it ultimately dictates the overall achieved performance. We present the folding mechanism, a process that combines measurements captured through minimal instrumentation and coarse-grain sampling ensuring low time dilation (less than 5%). The mechanism reports instantaneous performance and source-code references for optimized binaries accurately by taking advantage of the repetitiveness of many applications, especially in HPC. The mechanism enables the exploration of the application performance and guides the analyst to source-code modifications

Folding: reporting instantaneous performance metrics and source-code references

Despite supercomputers deliver huge computational power, applications only reach a fraction of it. There are several factors limiting the application performance, and one of the most important is the single processor efficiency because it ultimately dictates the overall achieved performance. We present the folding mechanism, a process that combines measurements captured through minimal instrumentation and coarse-grain sampling ensuring low time dilation (less than 5%). The mechanism reports instantaneous performance and source-code references for optimized binaries accurately by taking advantage of the repetitiveness of many applications, especially in HPC. The mechanism enables the exploration of the application performance and guides the analyst to source-code modifications

A fast, low-memory, and stable algorithm for implementing multicomponent transport in direct numerical simulations

Implementing multicomponent diffusion models in reacting-flow simulations is computationally expensive due to the challenges involved in calculating diffusion coefficients. Instead, mixture-averaged diffusion treatments are typically used to avoid these costs. However, to our knowledge, the accuracy and appropriateness of the mixture-averaged diffusion models has not been verified for three-dimensional turbulent premixed flames. In this study we propose a fast,efficient, low-memory algorithm and use that to evaluate the role of multicomponent mass diffusion in reacting-flow simulations. Direct numerical simulation of these flames is performed by implementing the Stefan-Maxwell equations in NGA. A semi-implicit algorithm decreases the computational expense of inverting the full multicomponent ordinary diffusion array while maintaining accuracy and fidelity. We first verify the method by performing one-dimensional simulations of premixed hydrogen flames and compare with matching cases in Cantera. We demonstrate the algorithm to be stable, and its performance scales approximately with the number of species squared. Then, as an initial study of multicomponent diffusion, we simulate premixed, three-dimensional turbulent hydrogen flames, neglecting secondary Soret and Dufour effects. Simulation conditions are carefully selected to match previously published results and ensure valid comparison. Our results show that using the mixture-averaged diffusion assumption leads to a 15% under-prediction of the normalized turbulent flame speed for a premixed hydrogen-air flame. This difference in the turbulent flame speed motivates further study into using the mixture-averaged diffusion assumption for DNS of moderate-to-high Karlovitz number flames.Comment: 36 pages, 14 figure

Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks

Near-wall regions in wall-bounded turbulent flows experience strong intermittent events involving ejections of slow-moving fluid parcels away from the wall, and `sweeps' of faster moving fluid towards the wall. Here, we train a three-dimensional Convolutional Neural Network (CNN) to predict the intensity of ejection events that occur in Direct Numerical Simulation (DNS) of a periodic channel flow. The trained network is able to predict burst intensities accurately for flow snaphshots that are sufficiently removed from the training data so as to be temporally decorrelated. More importantly, we probe the trained network to reveal regions of the flow where the network focuses its attention in order to make a prediction. We find that these salient regions correlate very well with fluid parcels being ejected away from the wall. Moreover, the CNN is able to keep track of the salient fluid parcels as the flow evolves in time. This demonstrates that CNNs are capable of discovering dynamically critical phenomena in turbulent flows without requiring any a-priori knowledge of the underlying dynamics. Remarkably, the trained CNN is able to predict ejection intensities accurately for data at different Reynolds numbers, which highlights its ability to identify physical processes that persist across varying flow conditions. The results presented here highlight the immense potential of CNNs for discovering and analyzing nonlinear spatial correlations in turbulent flows.Comment: 10 pages, 7 figure

Bio-inspired call-stack reconstruction for performance analysis

The correlation of performance bottlenecks and their associated source code has become a cornerstone of performance analysis. It allows understanding why the efficiency of an application falls behind the computer's peak performance and enabling optimizations on the code ultimately. To this end, performance analysis tools collect the processor call-stack and then combine this information with measurements to allow the analyst comprehend the application behavior. Some tools modify the call-stack during run-time to diminish the collection expense but at the cost of resulting in non-portable solutions. In this paper, we present a novel portable approach to associate performance issues with their source code counterpart. To address it, we capture a reduced segment of the call-stack (up to three levels) and then process the segments using an algorithm inspired by multi-sequence alignment techniques. The results of our approach are easily mapped to detailed performance views, enabling the analyst to unveil the application behavior and its corresponding region of code. To demonstrate the usefulness of our approach, we have applied the algorithm to several first-time seen in-production applications to describe them finely, and optimize them by using tiny modifications based on the analyses.We thankfully acknowledge Mathis Bode for giving us access to the Arts CF binaries, and Miguel Castrillo and Kim Serradell for their valuable insight regarding Nemo. We would like to thank Forschungszentrum Jülich for the computation time on their Blue Gene/Q system. This research has been partially funded by the CICYT under contracts No. TIN2012-34557 and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

A cost-effective semi-implicit method for the time integration of fully compressible reacting flows with stiff chemistry

We present a simple method to remove the stiffness associated with the chemical source terms in the fully compressible Navier-Stokes equations when the classical fourth order Runge-Kutta scheme is used