An On-Line Model for Predicting Residual Gas Fraction by Measuring Intake/Exhaust and Cylinder Pressure in CAI Engine

2008 ◽  
Author(s):  
Seungmok Choi ◽  
Minyoung Ki ◽  
Kyoungdoug Min ◽  
Jinkook Kong ◽  
Kyoungjoon Chang ◽  
...  
Keyword(s):  
Cylinder Pressure ◽  
Line Model ◽  
On Line ◽  
Residual Gas Fraction ◽  
Residual Gas

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.

A Model for On-Line Monitoring of In-Cylinder Residual Gas Fraction (RGF) and Mass Flowrate in Gasoline Engines

2006 ◽  
Author(s):  
Jing Ping Liu ◽  
Henning Kleeberg ◽  
Dean Tomazic ◽  
Joseph A. Ciaravino ◽  
Amer A. Amer
Keyword(s):  
Gasoline Engines ◽  
Mass Flowrate ◽  
On Line ◽  
Residual Gas Fraction ◽  
Residual Gas

Air Charge and Residual Gas Fraction Estimation for a Spark-Ignition Engine Using In-Cylinder Pressure

2017 ◽  
Author(s):  
Arya Yazdani ◽  
Jeffrey Naber ◽  
Mahdi Shahbakhti ◽  
Paul Dice ◽  
Chris Glugla ◽  
...  
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Cylinder Pressure ◽  
Residual Gas Fraction ◽  
Residual Gas

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.

On-Line Model Predictive Control for Energy Efficiency in Data Center

2021 ◽  
Vol 38 (12) ◽  
pp. 943-951
Author(s):  
Min Sik Chu ◽  
Hyun Ah Kim ◽  
Kyu Jong Lee ◽  
Ji Hoon Kang
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Data Center ◽  
Line Model ◽  
On Line
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