Evaluation of Ignition Timing Predictions Using Control-Oriented Models in Kinetically-Modulated Combustion Regimes

Goldsborough, S. Scott; Smith, Timothy A.; Johnson, Michael; McConnell, Steven S.

doi:10.4271/2012-01-1136

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…21 This kinetics model was chosen as it is the most detailed model for gasoline-type fuels that is readily available, and has been widely used as the basis for reduced mechanisms. [22][23][24][25][26] The fuel selected for this study is a four-component E10 87 anti-knock index (AKI) gasoline surrogate (toluene reference fuel with ethanol (TRF-E)). It was based on a ternary RD3-87 blend with the addition of ethanol.…”

Section: Evaluation Of Ai Modelsmentioning

confidence: 99%

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

Robertson

Prucka

2020

International Journal of Engine Research

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The drive to improve performance and efficiency of internal combustion engines has greatly expanded the degrees of freedom of engine systems. As efficiency objectives exceed the capability of traditional combustion strategies, advanced combustion modes are more attractive for production. These advanced combustion strategies typically add sensors, actuators, and degrees of freedom to the combustion process itself. Spark-assisted compression ignition is an efficient production-viable advanced combustion mode characterized by a spark-ignited flame propagation that triggers autoignition in the remaining unburned gas. This research focuses on autoignition modeling for spark-assisted compression ignition combustion phasing control. This work comprehensively evaluates several autoignition model structures and identifies the real-time production control implications of each. The candidate models include four ignition delay correlations, an ignition delay lookup, three polynomial regressions, and an artificial neural network. All are computationally feasible using production controllers, but the artificial neural network model represents autoignition phasing significantly better than the other options evaluated. The polynomial regressions were similar in error and exceeded the accuracy of ignition delay models. The low performance of the induction time integral–based models stems primarily from the exclusion of low-temperature heat release. The regression models are also exercised on an experimental engine dataset to identify the impact of engine phenomenon such as charge stratification on the performance of each model structure. The trends in the model performance as well as the magnitude of the error were similar when evaluated on both spark-assisted compression ignition simulation data and homogeneous charge compression ignition experimental data.

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Section: Evaluation Of Ai Modelsmentioning

confidence: 99%

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

Robertson

Prucka

2020

International Journal of Engine Research

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“…1,16 With regard to simplifying the prediction of ignition delay times using ignition correlation, some work based on chemical kinetic models has been presented in the literature. 17,18 For correlations based on experimental ignition data, most focus on empirical Arrhenius-type expressions There has also been sustained effort by some researchers to develop highly reduced chemical kinetic models based on the analysis of detailed and skeletal chemical kinetic models. A detailed analysis of temperature cross-over effects in n-heptane ignition was presented by Peters et al 19 Along this line, Saxena et al 20 developed a generalized ignition model for the prediction of high-temperature ignition delay times of n-alkanes, starting from a highly reduced skeletal mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…There is limited work in the literature on generalized correlations that covers a wide range of temperatures. 18 Considering the fact that most of the ignition correlations have been obtained only for high-temperature values, Hernandez et al 26 developed correlations of diesel fuel surrogates for each stage of ignition and demonstrated relationship of threshold temperatures between stages with pressure and equivalence ratio. Another generalized correlation approach is a recent empirical ignition correlation approach proposed by Vandersickel et al 27,28 It comprises separate correlations for the low-, high- and intermediate-temperature or negative temperature coefficient (NTC) regions which together can yield ignition delay predictions over the entire temperature range.…”

Section: Introductionmentioning

confidence: 99%

Simplifying ignition delay prediction for homogeneous charge compression ignition engine design and control

Zhou

Dong

Akih-Kumgeh

2016

International Journal of Engine Research

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Advanced combustion engine concepts, such as the homogeneous charge compression ignition engine, rely on chemical kinetics for their proper operation. Accurate prediction and control of auto ignition time scales are therefore key issues. Real-time prediction and control of ignition using detailed chemical kinetic models is difficult due to the large size of such mechanisms that calls for high computational costs. Ignition models in the form of correlations can overcome these challenges, as long as they are able to predict the time scales that would be obtained using a detailed chemical kinetic model. In this work, a correlation approach based on detailed chemical models is used to simplify the determination of chemical time scales associated with kinetically controlled combustion events. Detailed combustion mechanisms for biodiesel surrogate (methyl decanoate), gasoline surrogates (isooctane/n-heptane/toluene), bio-alcohol (n-octanol) and ethanol/gasoline surrogate blends from the literature are used to generate ignition databases for correlation development, taking into account complex ignition behavior such as pressure-dependent limits of negative temperature coefficient kinetics. We further employ the Livengood-Wu integral method to assess the ability of the resulting correlations to predict ignition in cases where the temperature and pressure are changing prior to ignition. The integral method is compared with results of a single-zone adiabatic homogeneous charge compression ignition engine model simulation using the detailed chemical kinetic model. It is found that the simplified correlations accurately reproduce the predictions of the detailed chemical kinetic models at an insignificant computational cost relative to the detailed simulations. The correlation approach presented here can also be applied to experimental ignition data, as far as these are obtained in a manner that covers a wide range of the relevant parameters with minimal experimental uncertainties. This work enables the incorporation of realistic chemical kinetic effects in the computational analysis and control of kinetically controlled combustion systems.

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Characterization of the chemical kinetics of dimethyl ether (DME) in a controlled trajectory - rapid compression and expansion machine (CT-RCEM)

Bavandla

Zhou

Tripathi

et al. 2023

Combustion and Flame

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Evaluation of Ignition Timing Predictions Using Control-Oriented Models in Kinetically-Modulated Combustion Regimes

Cited by 9 publications

References 52 publications

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

Evaluation of autoignition models for production control of a spark-assisted compression ignition engine

Simplifying ignition delay prediction for homogeneous charge compression ignition engine design and control

Characterization of the chemical kinetics of dimethyl ether (DME) in a controlled trajectory - rapid compression and expansion machine (CT-RCEM)

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