New experimental insights into acetylene oxidation through novel ignition delay times, laminar burning velocities and chemical kinetic modelling

Lokachari, Nitin; Burke, Ultan; Ramalingam, Ajoy; Turner, Mattias A.; Hesse, Raik; Somers, Kieran P.; Beeckmann, Joachim; Heufer, Karl Alexander; Petersen, Eric L.; Curran, Henry J.

doi:10.1016/j.proci.2018.07.027

Cited by 21 publications

(4 citation statements)

References 30 publications

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“…Despite the success in developing core mechanisms, considerable work remains to be done. A recent study of acetylene oxidation at high pressures and relatively low pressures shows very poor agreement of many common core mechanisms compared to new ignition delay data [145]. Figure 9 shows experimental ignition delay times measured at 10 bar in the NUI Galway shock tube [145] and comparisons with mechanism predictions from GRI-Mech 3.0 [3], San Diego Mech [64], [65], AramcoMech 2.0 [71], [72] and Glarborg Mech [73].…”

Section: Developing a Core High-temperature Mechanismmentioning

confidence: 99%

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Developing detailed chemical kinetic mechanisms for fuel combustion

Curran

2019

Proceedings of the Combustion Institute

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261

159

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This paper discusses a brief history of chemical kinetic modeling, with some emphasis on the development of chemical kinetic mechanisms describing fuel oxidation. At high temperatures, the important reactions tend to be those associated with the H2/O2 and C1-C2 sub-mechanisms, particularly for non-aromatic fuels. At low temperatures, and for aromatic fuels, the reactions that dominate and control the reaction kinetics are those associated with the parent fuel and its daughter radicals. Strategies used to develop and optimize chemical kinetic mechanisms are discussed and some reference is made to lumped and reduced mechanisms. The importance of accurate thermodynamic parameters for the species involved is also highlighted, as is the littlestudied importance of collider efficiencies of different third bodies involved in pressuredependent reactions.

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Section: Developing a Core High-temperature Mechanismmentioning

confidence: 99%

“…Acetylene IDT at φ = 1.0 in air at P = 10 bar. Experimental results (symbols), constant volume simulations (lines) of four different models from the literature[145]. Solid line -Glarborg Mech[73], dashed line -AramcoMech2.0[71],[72], dotted line -GRI-Mech 3.0[3], dash-dotted line -San Diego Mech [64],[65].…”

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confidence: 99%

Developing detailed chemical kinetic mechanisms for fuel combustion

Curran

2019

Proceedings of the Combustion Institute

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261

159

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“…Another factor could be the different geometries of the tubes, which have been shown to be impactful to the cell size [44]. Finally, the difference can also be due to the purity of the fuel from commercial cylinders, where acetylene gas is typically dissolved in acetone which may have an influence on the acetylene combustion characteristics [45].…”

Section: Acetylene-nitrous Oxide Mixtures (C 2 H-n 2 O)mentioning

confidence: 99%

Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

Bakalis,

2024

Energies

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In this work, a previously developed three-feature Artificial Neural Network (ANN) model with dimensional inputs is directly applied to predict the cell size of hydrocarbon/ammonia/nitrous oxide mixtures and compare these to experimental data. This model uses as inputs three ZND parameters (MCJ, ΔI, and σ˙max), which are mainly calculated using Konnov’s and Mével’s mechanisms. A similar prediction is obtained with the two mechanisms for the biogas–O2, H2–O2, H2–N2O, and NH3–O2 mixtures, indicating that the model is not only limited to Konnov’s chemical kinetic mechanism which was used for its training. The overall good agreement between the ANN predictions and the actual experimental values for the aforementioned mixtures, which are not used in the original training of the ANN model, is promising and shows its potential for application and extension to other mixtures and initial conditions, provided that the chemical kinetic parameters describing the ideal reaction zone structure could be computed. The model is then used to compare experimental cell size data from two detonation tube facilities, and also different chemical kinetic mechanisms for NH3-N2O mixtures. In the end, the original ANN model is expanded with the inclusion of additional cell size data, showing a slightly lower mean error for the predicted cell sizes if the data for the mixtures considered in this study are taken into account for the training of the new ANN model.

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“…At the same time, the recent study by Burke et al [13,14] investigated the oxidation of propene for a wide range of experimental conditions using different reactors and established a mechanism that represents the oxidation of propene well. The development of NUIGMech1.0 is a large undertaking with contributions from several experimental [9,10,[37][38][39][40] and theoretical studies [41][42][43][44], of which a few are referenced here. NUIGMech1.0 is hierarchically developed from hydrogen/oxygen to n-heptane chemistry.…”

Section: Model Development Comparison and Kinetic Analysismentioning

confidence: 99%