Skeletal chemical kinetic mechanism generation for methanol combustion and systematic analysis on the ignition characteristics

Liang, Jinhu; Jia, Wenlin; Sun, Yanjin; Wang, Quan‐De

doi:10.1002/apj.2434

Cited by 13 publications

(5 citation statements)

References 50 publications

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“…From the simulated point of view, firstly, the mechanisms of Li et al., 6 Pichler et al., 39 Christensen et al., 10 Liang et al., 40 and Liu et al 41 . deviate significantly from experimental measurements.…”

Section: Resultsmentioning

confidence: 99%

“…From the simulated point of view, firstly, the mechanisms of Li et al, 6 Pichler et al, 39 Christensen et al, 10 Liang et al, 40 and Liu et al 41 deviate significantly from experimental measurements. Second, the Burke mechanism 24 and skeletal mechanism slightly underestimate the flame speed in the region of Φ = 0.8-1.1, with deviations increase at higher initial temperatures.…”

Section: Laminar Flame Speedmentioning

confidence: 99%

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Laminar flame speed measurement and combustion mechanism optimization of methanol–air mixtures

Wang,

Zhang,

Zhong

2024

Int J of Chemical Kinetics

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Laminar flame speeds of methanol/air mixtures at 338–398 K are measured by the heat flux method, extending the range of equivalence ratio up to 2.1. And a new optimized methanol mechanism with 94 reactions is proposed by using the particle swarm algorithm, adjusting 20 Arrhenius pre‐exponential factors in their uncertainty domains. The optimized model is compared with eight methanol combustion mechanisms and experimental data published in recent years, covering a wide range of initial temperatures (298–1537 K), pressures (0.04–50 atm) and equivalence ratios (0.5–2.1). The results show that the optimized mechanism not only improves the accuracy of ignition delay time with rapid compression machine at low temperature but also moderately improve the description of laminar flame speed in lean and stoichiometric conditions. Meanwhile, the optimized model significantly enhances the prediction accuracy of CH3 and CH2O radical, and perfectly captures the evolution trend of HCO radical in laminar flat flame. Overall, the optimized mechanism provides the best overall description of the currently available measurements, leading to more accurate and comprehensive prediction of ignition delay time, laminar flame speed and species concentration.

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Section: Resultsmentioning

confidence: 99%

Section: Laminar Flame Speedmentioning

confidence: 99%

Laminar flame speed measurement and combustion mechanism optimization of methanol–air mixtures

Wang,

Zhang,

Zhong

2024

Int J of Chemical Kinetics

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“…Atomic-level simulations have contributed much to the understanding of the mechanisms of pyrolysis and combustion of hydrocarbons. − Reaction mechanisms and reaction rates can be extracted − from atomistic simulations such as MD simulations and compared directly to experimental data. Alternatively, this information can also be employed to parameterize a kinetic Monte Carlo (KMC) ,− model that can be used to reproduce chemical kinetics for longer time scales at a much reduced computational cost, as illustrated in Figure .…”

Section: Introductionmentioning

confidence: 99%

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

2021

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The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

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“…In addition, there are papers claiming to use CEMA, but in fact they cite CSP as its origin [35,41]; i.e., they cite the paper titled "Analysis of a Turbulent Lifted Hydrogen/Air Jet Flame from Direct Numerical Simulation with CSP " [58]. Moreover, in the paper [67] the CSP-based algorithmic tool "Importance Index " [20,48] is correctly referred as a CSP tool, but the "Participation Index " [18,69] is referred as a CEMA tool. Finally, CSP-based papers are characterized as CEMA-based ones [68].…”

Section: Introductionmentioning

confidence: 99%

The origin of CEMA and its relation to CSP

Goussis

Najm

et al. 2021

Combustion and Flame

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There currently exist two methods for analysing an explosive mode introduced by chemical kinetics in a reacting process: the Computational Singular Perturbation (CSP) algorithm and the Chemical Explosive Mode Analysis (CEMA). CSP was introduced in 1989 and addressed both dissipative and explosive modes encountered in the multi-scale dynamics that characterize the process, while CEMA was introduced in 2009 and addressed only the explosive modes. It is shown that (i) the algorithmic tools incorporated in CEMA were developed previously on the basis of CSP and (ii) the examination of explosive modes has been the subject of CSP-based works, reported before the introduction of CEMA.

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Skeletal chemical kinetic mechanism generation for methanol combustion and systematic analysis on the ignition characteristics

Cited by 13 publications

References 50 publications

Laminar flame speed measurement and combustion mechanism optimization of methanol–air mixtures

Laminar flame speed measurement and combustion mechanism optimization of methanol–air mixtures

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The origin of CEMA and its relation to CSP

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