Partially Stirred Reactor Model: Analytical Solutions and Numerical Convergence Study of a PDF/Monte Carlo Method

Bhave, Amit; Kraft, Markus

doi:10.1137/s1064827502411328

Cited by 35 publications

(17 citation statements)

References 18 publications

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“…and are integrated analytically to give (Kraft and Fey, 1999;Bhave and Kraft, 2004) The advantage of this solver is that it is very fast. It is general in the sense that it may be applied to any number of scalars, although it is restricted to N ¼ 2 particles.…”

Section: Eq (19) Is Rewritten Asmentioning

confidence: 99%

Numerical investigation of DQMoM-IEM as a turbulent reaction closure

Akroyd

Smith

McGlashan

et al. 2010

Chemical Engineering Science

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Section: Eq (19) Is Rewritten Asmentioning

confidence: 99%

Numerical investigation of DQMoM-IEM as a turbulent reaction closure

Akroyd

Smith

McGlashan

et al. 2010

Chemical Engineering Science

Self Cite

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“…Here, we describe our partially stirred reactor (PaSR) implementation for completeness, based on prior descriptions [27,[87][88][89][90][91]. The reactor model consists of an even number N part of particles, each with a time-varying composition φ(t).…”

Section: Appendix C Partially Stirred Reactor Implementationmentioning

confidence: 99%

pyJac: Analytical Jacobian generator for chemical kinetics

Niemeyer

Curtis

Sung

2017

Computer Physics Communications

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Accurate simulations of combustion phenomena require the use of detailed chemical kinetics in order to capture limit phenomena such as ignition and extinction as well as predict pollutant formation. However, the chemical kinetic models for hydrocarbon fuels of practical interest typically have large numbers of species and reactions and exhibit high levels of mathematical stiffness in the governing differential equations, particularly for larger fuel molecules. In order to integrate the stiff equations governing chemical kinetics, generally reactive-flow simulations rely on implicit algorithms that require frequent Jacobian matrix evaluations. Some in situ and a posteriori computational diagnostics methods also require accurate Jacobian matrices, including computational singular perturbation and chemical explosive mode analysis. Typically, finite differences numerically approximate these, but for larger chemical kinetic models this poses significant computational demands since the number of chemical source term evaluations scales with the square of species count. Furthermore, existing analytical Jacobian tools do not optimize evaluations or support emerging SIMD processors such as GPUs. Here we introduce pyJac, a Python-based open-source program that generates analytical Jacobian matrices for use in chemical kinetics modeling and analysis. In addition to producing the necessary customized source code for evaluating reaction rates (including all modern reaction rate formulations), the chemical source terms, and the Jacobian matrix, pyJac uses an optimized evaluation order to minimize computational and memory operations. As a demonstration, we first establish the correctness of the Jacobian matrices for kinetic models of hydrogen, methane, ethylene, and isopentanol oxidation (number of species ranging 13-360) by showing agreement within 0.001 % of matrices obtained via automatic differentiation. We then demonstrate the performance achievable on CPUs and GPUs using pyJac via matrix evaluation timing comparisons; the routines produced by pyJac outperformed first-order finite differences by 3-7.5 times and the existing analytical Jacobian software TChem by 1.1-2.2 times on a single-threaded basis. It is noted that TChem is not thread-safe, while pyJac is easily parallelized, and hence can greatly outperform TChem on multicore CPUs. The Jacobian matrix generator we describe here will be useful for reducing the cost of integrating chemical source terms with implicit algorithms in particular and algorithms that require an accurate Jacobian matrix in general. Furthermore, the open-source release of the program and Python-based implementation will enable wide * Corresponding author

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“…Finally, the turbulence-chemistry interaction has been modeled by the PaSR (Partially Stirred Reactor) combustion model. 25 3 Char gasification using a sectional approach…”

Section: −Yvsatmentioning

confidence: 99%

A Sectional Approach for the Entrained-Flow Gasification of Slurry Fuels

2018

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Energy densification is the overall objective of the on-going bioliq project. Bioslurry, obtained via fast pyrolysis of low-grade biogenic resources, is converted into high quality syngas in a high pressure entrained flow gasifier. The modeling of this three-phase system involving high pressure and high temperature sub-processes is very challenging. The detailed representation of the chemical sub-processes goes along with an increase of the computational cost. In this work, a novel approach is developed to achieve fast and accurate Computational Fluid Dynamics (CFD) simulations of the gasification of a slurry fuel in a laboratory entrained flow gasifier under atmospheric pressure. The method investigated relies on a sectional approach to describe the char gasification. An Euler-Euler approach is used for the modeling of the slurry/gas phase system. Ethylene glycol is used to represent the liquid part of the slurry. Experimental

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Partially Stirred Reactor Model: Analytical Solutions and Numerical Convergence Study of a PDF/Monte Carlo Method

Cited by 35 publications

References 18 publications

Numerical investigation of DQMoM-IEM as a turbulent reaction closure

Numerical investigation of DQMoM-IEM as a turbulent reaction closure

pyJac: Analytical Jacobian generator for chemical kinetics

A Sectional Approach for the Entrained-Flow Gasification of Slurry Fuels

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