Determination of Residual Gas Fraction in IC Engines

2003 ◽  
Author(s):  
F. Schwarz ◽  
U. Spicher
Keyword(s):  
Residual Gas Fraction ◽  
Ic Engines ◽  
Residual Gas

Predictions of Residual Gas Fraction in IC Engines

1996 ◽  
Author(s):  
P. K. Senecal ◽  
J. Xin ◽  
Rolf D. Reitz
Keyword(s):  
Residual Gas Fraction ◽  
Ic Engines ◽  
Residual Gas

Determination of Cycle Temperatures and Residual Gas Fraction for HCCI Negative Valve Overlap Operation

2010 ◽  
Vol 3 (1) ◽  
pp. 124-141 ◽  
Author(s):  
Russell P. Fitzgerald ◽  
Richard Steeper ◽  
Jordan Snyder ◽  
Ronald Hanson ◽  
Randy Hessel
Keyword(s):  
Residual Gas Fraction ◽  
Residual Gas ◽  
Negative Valve Overlap ◽  
Valve Overlap

Machine Learning Integration with Combustion Physics to Develop a Composite Predictive Model for Reactivity Controlled Compression Ignition Engine

2021 ◽  
pp. 1-27
Author(s):  
Chinmaya Mishra ◽  
P.M.V. Subbarao
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Alternative Methods ◽  
Compression Ignition ◽  
Combustion Control ◽  
Reactivity Controlled Compression Ignition ◽  
Operating Condition ◽  
Residual Gas Fraction ◽  
Residual Gas ◽  
Engine Operating Condition

Abstract Phasing of combustion metrics close to the optimum values across operation range is necessary to avail benefits of reactivity controlled compression ignition (RCCI) engines. Parameters like start of combustion occurrence crank angle (θsoc), occurrence of burn rate fraction reaching 50% (θ50), mean effective pressure from indicator diagram (IMEP) etc. are described as combustion metrics. These metrics act as markers for macroscopic state of combustion. Control of these metrics in RCCI engine is relatively complex due to the nature of ignition. As direct combustion control is challenging, alternative methods like combustion physics derived models are a subject of research interest. In this work, a composite predictive model was proposed by integrating trained random forest (RF) machine learning and artificial neural networks (ANN) to combustion physics derived modified Livengood-Wu integral, parametrized double-Wiebe function, autoignition front propagation speed based correlations and residual gas fraction model. The RF machine learning established a correlative relationship between physics based model coefficients and engine operating condition. The ANN developed a similar correlation between residual gas fraction parameters and engine operating condition. The composite model was deployed for the predictions of θsoc, θ50 and IMEP as RCCI engine combustion metrics. Experimental validation showed an error standard deviation (θ68.3,err) of 0.67 °CA, 1.19°CA, 0.223 bar and symmetric mean absolute percentage error of 6.92%, 7.87% and 4.01% for the predictions of θsoc, θ50 and IMEP respectively on cycle to cycle basis. Wide range applicability, lesser experiments for model calibration, low computational costs and utility for control applications were the benefits of the proposed predictive model.

scholarly journals Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 844 ◽  
Author(s):  
Seokwon Cho ◽  
Jihwan Park ◽  
Chiheon Song ◽  
Sechul Oh ◽  
Sangyul Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
Combustion Engine ◽  
Cylinder Pressure ◽  
Estimation Model ◽  
Experimental Conditions ◽  
Modeling And Analysis ◽  
Residual Gas Fraction ◽  
Residual Gas ◽  
The Individual

The knock phenomenon is one of the major hindrances for enhancing the thermal efficiency in spark-ignited engines. Due to the stochastic behavior of knocking combustion, analytical cycle studies are required. However, there are many problems to be addressed with regard to the individual cycle analysis of in-cylinder pressure data. This study thus proposes novel, comprehensive and efficient methodologies for evaluating the knocking combustion in the internal combustion engine. The proposed methodologies include a filtering method for the in-cylinder pressure, the determination of the knock onset, and the calculation of the residual gas fraction. Consequently, a smart knock onset model with high accuracy could be developed using a supervised deep learning that was not available in the past. Moreover, an improved zero-dimensional (0D) estimation model for the residual gas fraction was developed to obtain better accuracy for closed system analysis. Finally, based on a cyclic analysis, a knock prediction model is suggested; the model uses 0D ignition delay correlation under various experimental conditions including aggressive cam phase shifting by a dual variable valve timing (VVT) system. Using the proposed analysis method, insight into stochastic knocking combustion can be obtained, and a faster combustion speed can lead to a higher knock intensity in a steady-state operation.

scholarly journals Comparative Study of the Effective Release Energy, Residual Gas Fraction, and Emission Characteristics with Various Valve Port Diameter-Bore Ratios (VPD/B) of a Four-Stroke Spark Ignition Engine

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1330 ◽  
Author(s):  
Nguyen Xuan Khoa ◽  
Ocktaeck Lim
Keyword(s):  
Load Condition ◽  
Experimental System ◽  
Optimization Method ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Pressure Increase ◽  
Intake Port ◽  
Exhaust Port ◽  
Residual Gas Fraction ◽  
Residual Gas

In this research, the residual gas, peak firing pressure increase, and effective release energy were completely investigated. To obtain this target, the experimental system is installed with a dynamo system and a simulation model was setup. Through combined experimental and simulation methods, the drawbacks of the hardware optimization method were eliminated. The results of the research show that the valve port diameter-bore ratio (VPD/B) has a significant effect on the residual gas, peak firing pressure increase, and effective release energy of a four-stroke spark ignition engine. In this research, the engine was performed at 3000 rpm and full load condition. Following increased IPD/B ratio of 0.3–0.5. The intake port and exhaust port diameter has a contrary effect on engine volumetric efficiency, the residual gas ratio increase 27.3% with larger intake port and decrease 18.6% with larger exhaust port. The engine will perform optimal thermal efficiency when the trapped residual gas fraction ratio is from 13% to 14%. The maximum effective release energy was 0.45 kJ at 0.4 intake port-bore ratio, and 0.451 kJ at 0.35 exhaust port-bore ratio. The NOx emission increases until achieved a maximum value after that decrease even VPD/B was still increasing. With a VPD/B ratio of 0.35 to 0.4, the engine works without the misfiring.

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