Extreme-scaling Applications 24/7 on JUQUEEN Blue Gene/Q

Jülich Supercomputing Centre has offered Extreme Scaling Workshops since 2009, with the latest edition in February 2015 giving seven international code teams an opportunity to (im)prove the scaling of their applications to all 458752 cores of the JUQUEEN IBM BlueGene/Q. Each of them successfully adapted their application codes and datasets to the restricted compute-node memory and exploit the massive parallelism with up to 1.8 million processes or threads. They thereby qualified to become members of the High-Q Club which now has over 24 codes demonstrating extreme scalability. Achievements in both strong and weak scaling are compared, and complemented with a review of program languages and parallelisation paradigms, exploitation of hardware threads, and file I/O requirements

On the self-similarity of line segments in decaying homogeneous isotropic turbulence

The self-similarity of a passive scalar in homogeneous isotropic decaying turbulence is investigated by the method of line segments (M. Gauding et al., Physics of Fluids 27.9 (2015): 095102). The analysis is based on a highly resolved direct numerical simulation of decaying turbulence. The method of line segments is used to perform a decomposition of the scalar field into smaller sub-units based on the extremal points of the scalar along a straight line. These sub-units (the so-called line segments) are parameterized by their length $\ell$ and the difference $\Delta\phi$ of the scalar field between the ending points. Line segments can be understood as thin local convective-diffusive structures in which diffusive processes are enhanced by compressive strain. From DNS, it is shown that the marginal distribution function of the length~$\ell$ assumes complete self-similarity when re-scaled by the mean length $\ell_m$. The joint statistics of $\Delta\phi$ and $\ell$, from which the local gradient $g=\Delta\phi/\ell$ can be defined, play an important role in understanding the turbulence mixing and flow structure. Large values of $g$ occur at a small but finite length scale. Statistics of $g$ are characterized by rare but strong deviations that exceed the standard deviation by more than one order of magnitude. It is shown that these events break complete self-similarity of line segments, which confirms the standard paradigm of turbulence that intense events (which are known as internal intermittency) are not self-similar

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Tracking and analysis of flamelet structures in turbulent non-premixed combustion

In this thesis, two approaches to analyze flamelet structures in turbulent non-premixed combustion are presented. The analyses are based on a highly resolved direct numerical simulation (DNS) of a temporally evolving turbulent syngas jet flame that was conducted to this end. First, the simulation is analyzed by means of a novel on-the-fly tracking procedure to assess the model assumptions of a recently published extended flamelet formulation, which in contrast to the classical flamelet model, explicitly accounts for flame-tangential transport effects. After the extended model is modified to describe flamelets in a Lagrangian manner, flamelets are tracked in the DNS with the help of tracer particles that are attached to the iso-surface of stoichiometric mixture fraction. At each time step, gradient trajectories (flamelets) are emitted from these particles and are traced along the ascending and descending gradient until a local extremal point is reached. The on-the-fly tracking procedure thus allows a detailed analysis of single flamelets in turbulent flames and a full reconstruction of flamelet histories for the first time. In a second step, the history of flamelet identities is recomputed by means of a flamelet solver and a distinction is made between three different approaches (i) the classical flamelet (FLT), (ii) the curvature-affected flamelet (FLT-C) and (iii) the multi-dimensional flamelet (FLT-MD). Comparing the results of the recomputed histories with the DNS, it turns out that the FLT and FLT-C approaches mostly fail to reproduce the DNS results. On the other hand, the full extended FLT-MD approach agrees very well with the DNS for all identities considered, indicating that curvature-induced flame-tangential transport is an important aspect to consider in flamelet modeling. The study is complemented by a budget analysis of instantaneous flamelet identities, with which it is possible to quantify transient effects for the first time. The second approach analyzing flamelet structures consists in decomposing the mixture fraction field into small subunits called “dissipation elements”. Dissipation elements are defined as the ensemble of all gradient trajectories that end at the same local extremal points (minimum and maximum). However, in contrast to the on-the-fly tracking this procedure is applied during the postprocessing of the DNS and allows to identify all gradient trajectories. Originally developed for the statistical analysis of non-reacting flows, this methodology promotes novel flamelet-based modeling strategies. Classifying dissipation elements according to the location of their extremal points, statistics are computed and analyzed for two instants of time, considering the Euclidean distance l of the extremal points, the scalar difference ∆Z, the arithmetic mean Zm and an approximate gradient g = ∆Z/l . These statistics lead to further conclusions regarding the location of dissipation elements in mixture fraction space and how they are affected by turbulent mixing. Last, joint statistics of the temperature and the species mass fraction of OH with respect to g are inspected. It turns out, that there exists a strong correlation between the approximated gradient g and the quantities T and YOH, respectively

Direct Numerical Simulation of Fluid Turbulence at Extreme Scale with psOpen

The hybrid OpenMP/MPI code psOpen has been developed at the Institute for Combustion Technology, RWTH Aachen University, to study incompressible fluid turbulence by means of direct numerical simulations (DNS). For efficiency and accuracy psOpen employs a pseudo-spectral method, where the governing equations are solved partly in Fourier space. Hence, a pseudo-spectral method requires frequent transformations between real and Fourier space which is particularly challenging for massively-parallel setups. The frequently invoked Fourier transformation is a non-local operation and requires access to all data along a global grid line for each computational process. psOpen was improved by a new inhousedeveloped 3d-FFT library optimised to the special needs of pseudo-spectral DNS. For an optimal use of the hardware resources two techniques have been combined to reduce the communication time significantly. Firstly, the number of operations and the size of data to be transposed while computing a 3d-FFT has been reduced by integrating the dealiasing cut-off filter into the 3d-FFT. Secondly, the new 3d-FFT library allows to overlap communication and computation of multiple FFTs at the same time. The scaling performance of psOpen with three different grid sizes, namely 40963, 61443 and 81923, has been studied on JUQUEEN up to the full machine. © 2016 The authors and IOS Press. All rights reserved