Mixing and non-premixed combustion at supercritical pressures

This thesis is devoted to the numerical investigation of mixing and non- premixed combustion of cryogenic propellants at supercritical pressures. These severe conditions are commonly encountered in high pressure combustion chambers, such as those of liquid-fueled rocket engines (LRE), and lead to significant deviations from the ideal gas thermodynamic behavior of the reacting mixtures. The non-premixed laminar flame structure of liquid oxygen (LOx) and methane or liquid natural gas (LNG) mixtures, a recently proposed LRE propellants com- bination, is investigated by means of a general fluid unsteady flamelet solver. Real gas effects are analyzed on prototypical unsteady flame phenomena such as autoignition and re-ignition/quenching caused by strain perturbations. Such effects influence different flame regions depending on pressure, as well as the critical strain values that a laminar flame can sustain before quenching occurs. Moreover the flame structure is also influenced by the composition of the LNG, in particular the early stage soot precursors production and oxidation. In order to shed light on real gas mixing, a low-Mach approximation for real gas reacting mixtures is presented. A single species non-reacting real gas model is implemented in a highly scalable spectral element computational fluid dynamic (CFD) code with state of the art thermodynamic and transport properties. Transcritical and supercritical planar temporal jets, are chosen as representative test cases for investigating high-pressure mixing by means of direct numerical simulations. The pseudo-boiling phenomenon, occurring in transcritical flows, significantly influences the jet development, mitigating the development of shear layer instabilities and leading to a liquid-like jet break-up. Moreover pseudo-boiling is confined in a narrow spatial region suggesting particular care in the turbulent combustion modeling of non-premixed flames when transcritical thermodynamic conditions are encountered. The results of the present thesis, its physical insights as well as the modeling considerations involved, can be of support in the development of future CFD tools capable of simulating real engine operative conditions and configurations

Quantum algorithm for the computation of the reactant conversion rate in homogeneous turbulence

The rapid developments that have occurred in quantum computing platforms over the past few years raise important questions about the potential for applications of this new type of technology to fluid dynamics and combustion problems, and the timescales on which such applications might be possible. As a concrete example, here a quantum algorithm is developed and employed for predicting the rate of reactant conversion in the binary reaction of F + rO → (1 + r) in non-premixed homogeneous turbulence. These relations are obtained by means of a transported probability density function equation. The quantum algorithm is developed to solve this equation and is shown to yield the rate of the reactants' conversion much more efficiently than current classical methods, achieving a quadratic quantum speedup, in line with expectations for speedups arising from quantum metrology techniques more broadly. This provides an important example of a quantum algorithm with a real engineering application, which can build a connection to present work in turbulent combustion modelling and form the basis for further development of quantum computing platforms and their applications to fluid dynamics

HPC-enabling technologies for high-fidelity combustion simulations

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. }The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CoEC project, grant agreement No. 952181 and the CoE RAISE project grant agreement no. 951733.Peer ReviewedPostprint (published version

CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

This report documents the results of a study to address the long range, strategic planning required by NASA's Revolutionary Computational Aerosciences (RCA) program in the area of computational fluid dynamics (CFD), including future software and hardware requirements for High Performance Computing (HPC). Specifically, the "Vision 2030" CFD study is to provide a knowledge-based forecast of the future computational capabilities required for turbulent, transitional, and reacting flow simulations across a broad Mach number regime, and to lay the foundation for the development of a future framework and/or environment where physics-based, accurate predictions of complex turbulent flows, including flow separation, can be accomplished routinely and efficiently in cooperation with other physics-based simulations to enable multi-physics analysis and design. Specific technical requirements from the aerospace industrial and scientific communities were obtained to determine critical capability gaps, anticipated technical challenges, and impediments to achieving the target CFD capability in 2030. A preliminary development plan and roadmap were created to help focus investments in technology development to help achieve the CFD vision in 2030

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

A computational analysis of local flow for reacting Diesel sprays by means of an Eulerian CFD model

[EN] An implementation and validation of the coupled Sigma-gamma ADF model is presented in this work for reacting Diesel spray CFD simulations under a RANS turbulence modeling approach. An Approximated Diffusion Flamelet (ADF) model Michel et al. (2008) implemented in the OpenFOAM CFD open-source library by Winklinger (2014)15 fed with the spray description, i.e. mixing formation process, provided by the Sigma-gamma Eulerian atomization model Garcia-Oliver et al. (2013). In the present investigation, the Engine Combustion Network Spray A reference configuration is used for validation. Specifically, the model can provide accurate predictions of typical reacting spray metrics, such as the ignition delay and the lift-off length. Moreover, the internal structure is also fairly reproduced in terms of quasi-steady spatial distribution of formaldehyde and OH, related with low and high temperature reactions respectively. Additionally, modeling results have been compared to recent Particle image velocimetry (PIV) measurements Garcia-Oliver et al. (2017) under both inert and reacting conditions. Flow response to heat release is quantitatively predicted by the model, both in terms of local velocity increase as well as radial dilation. The model has been used to understand combustion-induced reduction in entrainment, in particular around the lift-off length location. Flow confinement does not seem to influence the global flame behaviour, even though some changes in the local flow hint can be observed when moving from an open to a closed domain. (C) 2017 Elsevier Ltd. All rights reserved.Authors acknowledge that this work was possible thanks to the Programa de Ayudas de Investigation y Desarrollo (PAID-2013 3198) of the Universitat Politecnica de Valencia. Also this study was partially funded by the Spanish Ministry of Economy and Competitiveness in the frame of the COMEFF(TRA2014-59483-R) project. Authors thank Gilles Bruneaux from IFPEN for the interesting suggestions and discussions.Pandal-Blanco, A.; García-Oliver, JM.; Novella Rosa, R.; Pastor Enguídanos, JM. (2018). A computational analysis of local flow for reacting Diesel sprays by means of an Eulerian CFD model. International Journal of Multiphase Flow. 99:257-272. https://doi.org/10.1016/j.ijmultiphaseflow.2017.10.010S2572729

StochSoCs: High performance biocomputing simulations for large scale Systems Biology

The stochastic simulation of large-scale biochemical reaction networks is of great importance for systems biology since it enables the study of inherently stochastic biological mechanisms at the whole cell scale. Stochastic Simulation Algorithms (SSA) allow us to simulate the dynamic behavior of complex kinetic models, but their high computational cost makes them very slow for many realistic size problems. We present a pilot service, named WebStoch, developed in the context of our StochSoCs research project, allowing life scientists with no high-performance computing expertise to perform over the internet stochastic simulations of large-scale biological network models described in the SBML standard format. Biomodels submitted to the service are parsed automatically and then placed for parallel execution on distributed worker nodes. The workers are implemented using multi-core and many-core processors, or FPGA accelerators that can handle the simulation of thousands of stochastic repetitions of complex biomodels, with possibly thousands of reactions and interacting species. Using benchmark LCSE biomodels, whose workload can be scaled on demand, we demonstrate linear speedup and more than two orders of magnitude higher throughput than existing serial simulators.Comment: The 2017 International Conference on High Performance Computing & Simulation (HPCS 2017), 8 page