Modeling of Cyclic Variations in Spark-Ignition Engines

Dai, Wen; Trigui, N.; Lu, Yi

doi:10.4271/2000-01-2036

Cited by 39 publications

(16 citation statements)

References 18 publications

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“…This variation could be caused by multiple factors such as differences in the amount of hot residual mass trapped from the previous cycle that would influence the temperature at the time of spark and transition to autoignition [22]. Additionally, bulk and turbulent flow fluctuations can affect flame stretch hence the duration of flame based combustion [23].…”

Section: Cyclic Variability Investigationmentioning

confidence: 99%

A Phenomenological Model for Predicting the Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines

Prakash

Martz

Stefanopoulou

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

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An advanced combustion mode, Spark Assisted Compression Ignition (SACI) has shown the ability to extend loads relative to Homogenous Charge Compression Ignition (HCCI) combustion but at reduced fuel conversion efficiency. SACI combustion is initiated by a spark, with flame propagation followed by a rapid autoignition of the remaining end-gas fuel fraction. Extending upon previous work [1,2], the Wiebe function coefficients used to fit the two combustion phases are regressed here as functions of the air path variables and actuator settings. The parameterized regression model enables mean-value modeling and model-based combustion phasing control. SACI combustion however, exhibits high cyclic variability with random characteristics. Thus, combustion phasing feedback control needs to account for the cyclic variability to correctly filter the phasing data. This paper documents the success in regressing the cyclic variability (defined as the standard deviation in combustion phasing) at various operating conditions, again as a function of air path variables and actuator settings. The combination of the regressed mean and standard deviation models is a breakthrough in predicting the mean-value engine behavior and the random statistics of the cycle-to-cycle variability.

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Section: Cyclic Variability Investigationmentioning

confidence: 99%

A Phenomenological Model for Predicting the Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines

Prakash

Martz

Stefanopoulou

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

View full text Add to dashboard Cite

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“…In the first study, a linear regression of COV of IMEP was developed using 146 data points obtained from three different chamber geometries, varying the total exhaust gas recirculation (EGR), air-fuel ratio, spark timing, engine speed and fueling level using a single-cylinder 0.6 liter displacement engine [22]. Using a wide range of engine geometries and operating conditions, The COV of IMEP was found to have a non-linear correlation to the engine geometry and operating conditions [23].…”

Section: I6 Research Contributions and Significant Findingsmentioning

confidence: 99%

“…The following compares the COV of gross IMEP correlation developed in this dissertation to the existing COV of gross IMEP correlation [23] Each chapter focuses on a different subject and has its own introduction, discussion, and conclusion. The first page of each chapter contains summary of that chapter.…”

Section: I6 Research Contributions and Significant Findingsmentioning

confidence: 99%

“…The COV of IMEP is commonly used as a metric to quantify the cycle variation limits and trends. In the literature, Two correlations of COV of IMEP are found [22,23]. In the first study, a linear regression of COV of IMEP was developed using 146 data point covering three different chamber geometries and varying the total exhaust gas recirculation (EGR), air-fuel ratio, spark timing, engine speed and fueling level using a single-cylinder 0.6 liter engine [22].…”

Section: Introduction I1 Backgroundmentioning

confidence: 99%

“…In the first study, a linear regression of COV of IMEP was developed using 146 data point covering three different chamber geometries and varying the total exhaust gas recirculation (EGR), air-fuel ratio, spark timing, engine speed and fueling level using a single-cylinder 0.6 liter engine [22]. In the second study, a non-linear regression of a polynomial form for COV of IMEP was developed as a function of engine speed and load, equivalence ratio, residual fraction, burn duration of 0-10%, burn duration of 10-90% and location of 50% mass fraction burn (MFB) using 6000 operating conditions collected from 13 different engines from 1.6 to 4.6 liters in displacement [23]. Although this correlation was developed using a wider range of data as compare to the first study, this regression computed negative COVs of IMEP within the range of data used in the correlation.…”

Section: Introduction I1 Backgroundmentioning

confidence: 99%

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