60 research outputs found

    Parallel chemistry acceleration algorithm with ISAT table-size control in the application of gaseous detonation

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    In order to improve the computational efficiency of a parallel ISAT (in situ adaptive tabulation)-based chemistry acceleration algorithm in the computations of transient, chemically reacting flows, a control strategy is proposed to maintain the sizes of the data tables in the ISAT computations. The table-size control strategy is then combined with a parallel algorithm to simulate two-dimensional gaseous detonation wave propagation. In the computation of 2H2 + O2 detonation, two sets of tests are conducted to identify the size control strategy. In the first set, the maximum total table size (Mtot) summed over all sub-zones is fixed, while the maximum size of the table on each sub-zone (Msin) is varied. In the second set, a fixed Msin is used for all the tables on the sub-zones while Mtot is varied. A maximum speedup ratio of 4.29 is found in the former tests, while 5.52 is found in the latter. Two parameters, σf and p, are proposed to analyze the load balance and synchronization among table operations in the parallel ISAT computations in the above tests. It is found that both load balance and synchronization have clear influences on the speedup ratio. A parameter pM is defined, and a strategy to choose the optimal maximum table sizes (both Mtot and Msin) based on pM is proposed and is verified to be universal in the computations of both 2H2 + O2 detonation and C2H4 + 3O2 detonation. Finally, the parallel acceleration algorithm enhanced with table-size control is shown to be highly accurate for the detonations in both fuels

    Development of virtual kinetics chemistry for the prediction of ignition delay time of a fuel presenting negative temperature coefficient behavior

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    Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-Graduação em Engenharia MecĂąnica, FlorianĂłpolis, 2022.A simulação de escoamentos reativos complexos em aplicaçÔes com combustĂŁo geralmente requer uma grande quantidade de poder computacional, e a parte reativa da solução Ă© geralmente a que consome o maior tempo. Mecanismos detalhados de cinĂ©tica quĂ­mica podem conter mais de dezenas de milhares de espĂ©cies quĂ­micas, enquanto que simulaçÔes numĂ©ricas de escoamentos reativos complexos estĂŁo limitadas a mecanismos cinĂ©ticos com, no mĂĄximo, algumas centenas de espĂ©cies. Mecanismos virtuais aparecem como uma estratĂ©gia muito eficiente para reduzir drasticamente o tempo computacional na parte reativa das simulaçÔes numĂ©ricas. Tais mecanismos sĂŁo formados por espĂ©cies e reaçÔes quĂ­micas artificiais, as quais sĂŁo otimizadas para reproduzir as caracterĂ­sticas importantes de um problema canĂŽnico de interesse. Esta tese apresenta o desenvolvimento de mecanismos virtuais para reproduzir a evolução da temperatura e o tempo de atraso de ignição para a ignição tĂ©rmica em um reator homogĂȘneo, adiabĂĄtico, com massa e pressĂŁo constantes de uma mistura ar-combustĂ­vel. O desenvolvimento dos mecanismos virtuais de ignição tĂ©rmica foi feita em duas etapas: a primeira para a ignição tĂ©rmica em alta temperatura e a segunda para a ignição tĂ©rmica em temperaturas baixas e intermediĂĄrias de uma mistura com NTC. Para a primeira etapa, modelou-se a ignição tĂ©rmica de uma mistura de metano e ar, com condiçÔes inicias de temperatura de 1000 K Ă  1500 K e pressĂ”es de 1 atm atĂ© 3 atm. Para a segunda etapa, modelou-se uma mistura de n-heptano e ar, com temperaturas de 600 K atĂ© 1500 K, em pressĂŁo atmosfĂ©rica. A primeira etapa apresenta algumas limitaçÔes ligadas, sobretudo, ao limitado nĂșmero de reaçÔes e consequentemente parĂąmetros do mecanismo virtual. A segunda etapa resolve algumas dessas limitaçÔes enquanto que, ao mesmo tempo, aumenta a aplicabilidade da metodologia. Ainda que os erros observados nos atrasos de ignição para os mecanismos virtuais aqui desenvolvidos sejam relativamente elevados, mecanismos reduzidos atravĂ©s de tĂ©cnicas mais comuns, como DRG, caso produzissem mecanismos com tamanhos similares, apresentariam erros muito superiores. A redução de tempo computacional na simulação com mecanismos reduzidos atinge valores atĂ© 1300 vezes.Abstract: The simulation of complex reactive flows in combustion applications generally requires a large amount of computational power, and the reactive part of the solution is usually the most time consuming. Detailed chemical kinetics mechanisms can reach up to tens of thousands species, while numerical simulations of complex reactive flows can handle mechanisms, at most, with a few hundred species. Virtual kinetic mechanisms are a very effective strategy to drastically reduce the computational time spent in the reactive part of numerical simulations. They are formed by artificial species and reaction paths, that are optimized to reproduce the important characteristics of a canonical problem of interest. This thesis presets the development of virtual mechanisms able to reproduce the temperature evolution and ignition delay times of the thermal ignition of a homogeneous, constant mass, constant pressure, adiabatic, fuel-air mixture reactor. The results are divided into two sections: the first one being related to methane-air mixture over a range of temperatures from 1000 K to 1500 K and pressures from 1 atm to 3 atm as a way to test the methodology developed. The second section presents an expansion to the methodology to cover complex fuel behaviors, such as the NTC, using a n-heptane-air mixture tested over a range of temperatures from 600 K to 1500 K and atmospheric pressures. The first section present some limitations of the most due to the small number of reactions and parameters of the kinetic model. The second section address some of those issues while further improving the virtual mechanism methodology applicability. The current IDT errors observed, although having large values, are much smaller than reduced mechanism produced with common strategies such as DRG of the same size

    Computationally-Efficient And Scalable Implementation Of Chemistry In Large-Scale Simulations Of Turbulent Combustion

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    A major challenge in the numerical simulations of turbulent reacting flows involving large numbers of chemical species and reactions is the accurate and computationally-efficient representation of combustion chemistry. Recent advances on the experimental and theoretical fronts of the study of real fuel chemistry have led to more accurate chemical mechanisms of real fuels involving hundreds to thousands of species and thousands of reactions. However, the direct use of such detailed chemistry in large-scale calculations of turbulent reacting flows still remains computationally prohibitive. In our work, we focus on the combined Large-Eddy Simulation (LES)/Probability Density Function (PDF) computations of turbulent reacting flows, in which the thermochemical composition of the fluid is represented by a large number of particles. To reduce the cost of chemistry calculations in large-scale LES/PDF computations, we have developed a combined dimension reduction and tabulation approach in which the chemistry is represented accurately and efficiently in terms of a reduced number of "represented species". In this combined approach, the dimension reduction of combustion chemistry is performed using the Rate-Controlled Constrained-Equilibrium (RCCE) method, followed by tabulation using the In Situ Adaptive Tabulation (ISAT) algorithm. An automated Greedy Algorithm with Local Improvement (GALI) has been developed for selecting good rep- resented species for use in this approach. In addition, we have developed a Partitioned Uniform Random (P-URAN) parallel strategy for the efficient parallel implementation of chemistry in large-scale LES/PDF simulations on multiple cores. This strategy has been tested by performing full-scale LES/PDF simulations of the Sandia Flame D turbulent jet flame on up to 9,216 cores and it is found to achieve good scaling. In this work (1) we describe in detail the implementation of ISAT/RCCE/GALI and the P-URAN parallel strategy; (2) we show that the combined ISAT/RCCE/GALI yields orders of magnitude speed-up with very good error control; (3) we demonstrate that our implementation of RCCE is an accurate, efficient and robust implementation; (4) we show that the P-URAN parallel strategy achieves over 85% relative weak scaling efficiency and around 60% relative strong scaling efficiency on up to 9,216 cores; (5) we show that the combined ISAT/RCCE methodology with P-URAN significantly reduces the simulation time; and (6) a combination of ISAT/RCCE and PURAN algorithms enables us to perform accurate and computationally-efficient large-scale LES/PDF simulations with real fuel chemistry involving hundreds of chemical species

    HPC-enabling technologies for high-fidelity combustion simulations

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    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

    Machine Learning and Its Application to Reacting Flows

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    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

    Get PDF
    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

    Approximation methodologies for explicit model predictive control of complex systems

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    This thesis concerns the development of complexity reduction methodologies for the application of multi-parametric/explicit model predictive (mp-MPC) control to complex high fidelity models. The main advantage of mp-MPC is the offline relocation of the optimization task and the associated computational expense through the use of multi-parametric programming. This allows for the application of MPC to fast sampling systems or systems for which it is not possible to perform online optimization due to cycle time requirements. The application of mp-MPC to complex nonlinear systems is of critical importance and is the subject of the thesis. The first part is concerned with the adaptation and development of model order reduction (MOR) techniques for application in combination to mp-MPC algorithms. This first part includes the mp-MPC oriented use of existing MOR techniques as well as the development of new ones. The use of MOR for multi-parametric moving horizon estimation is also investigated. The second part of the thesis introduces a framework for the ‘equation free’ surrogate-model based design of explicit controllers as a possible alternative to multi-parametric based methods. The methodology relies upon the use of advanced data-classification approaches and surrogate modelling techniques, and is illustrated with different numerical examples.Open Acces
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