Exploring the stochastic and deterministic aspects of cyclic emission variability on a high speed spark-ignition engine

Karvountzis-Kontakiotis, Apostolos; Dimaratos, Athanasios; Ntziachristos, Leónidas

doi:10.1016/j.energy.2016.12.026

Cited by 31 publications

(22 citation statements)

References 36 publications

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“…Combustion stability is a prerequisite for more efficient spark ignition (SI) engine operation [1,2] and reduced engine-out emissions [3,4]. However, the reduction of cycle-to-cycle variations (CCV) by design or control measures is challenging due to the complex multi-physics and multi-scale nature of the in-cylinder processes, which are not completely understood.…”

Section: Introductionmentioning

confidence: 99%

DNS study of the global heat release rate during early flame kernel development under engine conditions

Falkenstein

Kang

Cai

et al. 2020

Combustion and Flame

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Despite the high technical relevance of early flame kernel development for the reduction of cycle-to-cycle variations in spark ignition engines, there is still a need for a better fundamental understanding of the governing in-cylinder phenomena in order to enable resilient early flame growth. To isolate the effects of small-and large-scale turbulent flow motion on the young flame kernel, a three-dimensional DNS database has been designed to be representative for engine part load conditions. The analysis is focussed on flame displacement speed and flame area in order to investigate effects of flame structure and flame geometry on the global burning rate evolution. It is shown that despite a Karlovitz number of up to 13, which is at the upper range of conventional engine operation, thickening of the averaged flame structure by small-scale turbulent mixing is not observed. After ignition effects have decayed, the flame normal displacement speed recovers the behavior of a laminar unstretched premixed flame under the considered unity-Lewis-number conditions. Run-to-run variations in the global heat release rate are shown to be primarily caused by flame kernel area dynamics. The analysis of the flame area balance equation shows that turbulence causes stochastic flame kernel area growth by affecting the curvature evolution, rather than by inducing variations in total flame area production by strain. Further, it is shown that in local segments of a fully-developed planar flame with similar surface area as the investigated flame kernels, temporal variations in flame area rate-of-change occur. Contrasting to early flame kernels, these effects can be exclusively attributed to curvature variations in negatively curved flame regions.

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Section: Introductionmentioning

confidence: 99%

DNS study of the global heat release rate during early flame kernel development under engine conditions

Falkenstein

Kang

Cai

et al. 2020

Combustion and Flame

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“…As Figure 8 shows, AFR is indicated as air to fuel equivalence ratio, lambda ( λ ), which is the ratio of actual AFR to the stoichiometric value 1,2,36–38…”

Section: Methodsmentioning

confidence: 99%

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Zhou

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

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“…The cyclic variability effects on emissions have started recently to attract much attention, with the aim to determine their variation [14,39]. The work here focuses on NO and CO emissions, which are the main pollutants for SI engines.…”

Section: Cyclic Variability Of Emissionsmentioning

confidence: 99%

“…To this aim, a large diversity of experiments have been conducted focusing on a variety of parameters [8], such as the measurement of the velocity field, flame propagation and specifications [9][10][11][12], and the heat losses to the electrodes of the spark plug [13]. Recently, large focus was also given on the variability of emissions, which requires the use of advanced measuring equipment with a high sampling rate to capture the highly transient effects [14].…”

Section: Introductionmentioning

confidence: 99%

A Fast CFD-Based Methodology for Determining the Cyclic Variability and Its Effects on Performance and Emissions of Spark-Ignition Engines

Kosmadakis

Rakopoulos

2019

Energies

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A methodology for determining the cyclic variability in spark-ignition (SI) engines has been developed recently, with the use of an in-house computational fluid dynamics (CFD) code. The simulation of a large number of engine cycles is required for the coefficient of variation (COV) of the indicated mean effective pressure (IMEP) to converge, usually more than 50 cycles. This is valid for any CFD methodology applied for this kind of simulation activity. In order to reduce the total computational time, but without reducing the accuracy of the calculations, the methodology is expanded here by simulating just five representative cycles and calculating their main parameters of concern, such as the IMEP, peak pressure, and NO and CO emissions. A regression analysis then follows for producing fitted correlations for each parameter as a function of the key variable that affects cyclic variability as has been identified by the authors so far, namely, the relative location of the local turbulent eddy with the spark plug. The application of these fitted correlations for a large number of engine cycles then leads to a fast estimation of the key parameters. This methodology is applied here for a methane-fueled SI engine, while future activities will examine cyclic variations in SI engines when fueled with different fuels and their mixtures, such as methane/hydrogen blends, and their associated pollutant emissions.

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Exploring the stochastic and deterministic aspects of cyclic emission variability on a high speed spark-ignition engine

Cited by 31 publications

References 36 publications

DNS study of the global heat release rate during early flame kernel development under engine conditions

DNS study of the global heat release rate during early flame kernel development under engine conditions

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

A Fast CFD-Based Methodology for Determining the Cyclic Variability and Its Effects on Performance and Emissions of Spark-Ignition Engines

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