Modeling Autoignition and Engine Knock Under Spark Ignition Conditions

Eckert, P.; Kong, Song‐Charng; Reitz, Rolf D.

doi:10.4271/2003-01-0011

Cited by 33 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational fluid dynamics (CFD) modelling coupled with detailed combustion chemical kinetic mechanisms showed a potential to mimic the engine knock by considering temporal and spatial variance of the properties of the unburned gas mixtures, but requires massive computation resources [10]. Single-step [11,12] and highly simplified multi-step kinetic mechanisms [13,14,15] have -2 -been linked into the CFD simulations to simplify the calculations, but still computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

A Zero-Dimensional Combustion Model with Reduced Kinetics for SI Engine Knock Simulation

Liu¹,

Chen²

2009

Combustion Science and Technology

View full text Add to dashboard Cite

High load performance and fuel economy of gasoline engines are limited by knocks. Such limitations are becoming worse when the engine is heavily super-charged for high BMEP outputs. Spark ignition timing retardation has been an efficient method to avoid the knock but results in reduced engine performance and poor fuel economy. A better understanding of knock, which could be used to optimize the engine design, ignition timing optimization in particular, is important. In this research, a simulation model for SI engine knock has been developed. The model is based on a three-zone approach

show abstract

Section: Introductionmentioning

confidence: 99%

A Zero-Dimensional Combustion Model with Reduced Kinetics for SI Engine Knock Simulation

Liu¹,

Chen²

2009

Combustion Science and Technology

View full text Add to dashboard Cite

show abstract

“…Duclos et al (1996) simulated combustion and emissions in an SI engine using KIVA and found the effect of a residual gas and spark plug situation on the engine performance. Eckert et al (2003) simulated knock by KIVA-3V. They validated their results with an experimental test.…”

Section: Introductionmentioning

confidence: 83%

Numerical investigation on the effect of different parameters on the performance and the emission of a spark ignition (SI) engine

Mirgolbabaei¹,

Bozorgi²,

Etghani³

2013

Int. J. Phys. Sci.

View full text Add to dashboard Cite

In this work, NISSAN Z24 engine was simulated numerically and valves and ignition timings were modified in order to improve the engine performance and emissions. The engine was simulated using a version of the Los Alamos code KIVA-3V2 which is a computer program for the numerical calculation of transient, two and three dimensional chemically reactive flows with sprays. The computational grid was generated by the commercial software, ANSYS ICEM CFD. To validate the code, a comparison between experimental and theoretical results has been made, which confirm good qualitative agreement between these results. A series of parametric studied is performed to gain a better understanding of the effects of valves and ignition timings on the engine performance and emission. The results show that the engine volumetric efficiency, output power, emissions and performance are well improved by applying the optimal values.

show abstract

“…NOx emission [26,28,30,137] Exhaust aftertreatment system [145] Combustion mode transition [53] Airpath of turbocharged engines [32,34,52,141,144] Non-Convex Cyclic Variability [54,152] Soot and uHC emissions [153,154] Knock and MPRR [155][156][157] Engine dynamics have different time scales as shown in Figure 8. The response time of the controlled dynamics is a critical factor in design of the appropriate MPC framework.…”

Section: Convex or Convexifiablementioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

View full text Add to dashboard Cite

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

show abstract

Modeling Autoignition and Engine Knock Under Spark Ignition Conditions

Cited by 33 publications

References 21 publications

A Zero-Dimensional Combustion Model with Reduced Kinetics for SI Engine Knock Simulation

A Zero-Dimensional Combustion Model with Reduced Kinetics for SI Engine Knock Simulation

Numerical investigation on the effect of different parameters on the performance and the emission of a spark ignition (SI) engine

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Contact Info

Product

Resources

About