Numerical investigation of strained extinction at engine-relevant pressures: Pressure dependence and sensitivity to chemical and physical parameters for methane-based flames

Long, Alan E.; Speth, Raymond L.; Green, William

doi:10.1016/j.combustflame.2018.12.034

Cited by 17 publications

(10 citation statements)

References 56 publications

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“…Implementation of the calibration procedure requires that the normalized line profiles Δ ̅ to be known for Kr and H-atom (Eq. [2]). Since such determination is generally imprecise, the fluorescence signal was therefore spectrally integrated by scanning the laser wavelength over the resonance of each species following the recommendations of Goehlich et al [52].…”

Section: Quantification Of Tplif Signal In the Calibration Flamementioning

confidence: 99%

“…As with many small radicals, knowing their absolute concentration within the flame is important to better understand the kinetic reactions and detailed mechanisms. In stretched flames, the extinction strength rate and laminar flame speed directly interfere with these two reactions [1,2]. The reactions of formation and consumption of H-atoms compete strongly depending on the flame strain rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Lamoureux

Foo

Desgroux

2021

Combustion and Flame

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Section: Quantification Of Tplif Signal In the Calibration Flamementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Lamoureux

Foo

Desgroux

2021

Combustion and Flame

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“…3 suggest that learning the HyChem model from the ignition delay time dataset might be sufficient to develop a model that works well for both ignition and flame propagation with the help of regularization. Such generalization capability is especially useful recognizing that experimental measurements of laminar flame speed are usually limited to near-atmospheric pressures due to the onset of turbulence at elevated pressures [37,38]. Nevertheless, the SGD approach can potentially enable us to develop a data-driven kinetic model by providing only the high-pressure ignition delay time dataset but can work well for predicting both ignition and flame propagation at engine relevant elevated pressures.…”

Section: Overview Of Learning Resultsmentioning

confidence: 99%

Machine Learning Approaches to Learn HyChem Models

Ji,

Zanders,

Park

et al. 2021

Preprint

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The HyChem (Hybrid Chemistry) approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. Determining the pyrolysis submodel requires extensive experimentation on speciation measurements. Recent work has been directed to learn HyChem from an existing HyChem model for a similar fuel, which requires less data. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than hightemperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learn- * Address all correspondence to this author.ing, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.

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“…For X F = 0.4 shown here, the predicted extinction strain rate a E based on GRI 3.0 is 769 s –1 , based on Warnatz is 896 s –1 , based on FFCM-1 is 654 s –1 , and based on Sandiego-2014 is 913 s –1 , and the largest deviations can be up to 259 s –1 . For the physical and chemical investigations on large deviations between different mechanisms, one can refer to, for example, ref .…”

Section: Combustion Systemmentioning

confidence: 99%

Automatic Construction of REDIM Reduced Chemistry with a Detailed Transport and Its Application to CH₄ Counterflow Flames

et al. 2020

Energy Fuels

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Extinction limits are important quantities of counterflow diffusion flames. An accurate prediction of extinction limits is necessary for the design of engineering combustion devices involving flame quenching. In this work, the reaction−diffusion manifold (REDIM) reduced chemistry with a detailed transport model is applied for the numerical investigation of extinction limits of counterflow diffusion flames. Unlike other tabulated flamelet models where very detailed information about a particular combustion system is required, the REDIM reduced chemistry can be generated based on the detailed reaction mechanisms, requiring only a minimal additional knowledge of the considered combustion system. Recently, an automatic generation of the REDIM has been introduced and tested for premixed flames. This newly developed algorithm starts with a 1D reduced model, and any higher dimension of the REDIM reduced model can be constructed automatically without any additional information. Such an algorithm largely simplifies the generation of the REDIM reduced chemistry. The focus of this work is to apply this newly developed algorithm for the construction of two-dimensional (2D) and three-dimensional (3D) REDIMs for counterflow diffusion flames. It is shown how 2D and 3D REDIM reduced chemistry can be generated automatically in a generic way according to a hierarchical concept. An oxygen-enriched MILD combustion system CH 4 /CO 2 versus the O 2 /CO 2 counterflow diffusion flame, whose extinction strain rates had been measured experimentally, is selected as an illustrative example for discussion and validation. The relative errors of predicted extinction strain rates using a 3D REDIM are much less than the experimental uncertainty and the differences using different detailed chemical mechanisms.

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Numerical investigation of strained extinction at engine-relevant pressures: Pressure dependence and sensitivity to chemical and physical parameters for methane-based flames

Cited by 17 publications

References 56 publications

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Machine Learning Approaches to Learn HyChem Models

Automatic Construction of REDIM Reduced Chemistry with a Detailed Transport and Its Application to CH₄ Counterflow Flames

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Numerical investigation of strained extinction at engine-relevant pressures: Pressure dependence and sensitivity to chemical and physical parameters for methane-based flames

Cited by 17 publications

References 56 publications

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Quantitative measurement of atomic hydrogen in low-pressure methane flames using two-photon LIF calibrated by krypton

Machine Learning Approaches to Learn HyChem Models

Automatic Construction of REDIM Reduced Chemistry with a Detailed Transport and Its Application to CH4 Counterflow Flames

Contact Info

Product

Resources

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Automatic Construction of REDIM Reduced Chemistry with a Detailed Transport and Its Application to CH₄ Counterflow Flames