A Novel Model for Computing the Trapping Efficiency and Residual Gas Fraction Validated with an Innovative Technique for Measuring the Trapping Efficiency

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.

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Modeling HCCI Combustion With High Levels of Residual Gas Fraction - A Comparison of Two VVA Strategies

10.4271/2003-01-3220 ◽

2003 ◽

Cited By ~ 77

Author(s):

Aristotelis Babajimopoulos ◽

George A. Lavoie ◽

Dennis N. Assanis

Keyword(s):

Hcci Combustion ◽

Residual Gas Fraction ◽

Residual Gas

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

Energies ◽

10.3390/en12050844 ◽

2019 ◽

Vol 12 (5) ◽

pp. 844 ◽

Cited By ~ 2

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.

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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 ◽

10.3390/en13061330 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1330 ◽

Cited By ~ 3

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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Study on the Impacts of Capillary Number and Initial Water Saturation on the Residual Gas Distribution by NMR

Energies ◽

10.3390/en12142714 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2714

Author(s):

Tao Li ◽

Ying Wang ◽

Min Li ◽

Jiahao Ji ◽

Lin Chang ◽

...

Keyword(s):

Capillary Number ◽

Water Saturation ◽

Transverse Relaxation Time ◽

Spontaneous Imbibition ◽

Trapping Efficiency ◽

Gas Distribution ◽

Initial Water ◽

Capillary Trapping ◽

Residual Gas ◽

The Difference

The determination of microscopic residual gas distribution is beneficial for exploiting reservoirs to their maximum potential. In this work, both forced and spontaneous imbibition (waterflooding) experiments were performed on a high-pressure displacement experimental setup, which was integrated with nuclear magnetic resonance (NMR) to reveal the impacts of capillary number (Ca) and initial water saturation (Swi) on the residual gas distribution over four magnitudes of injection rates (Q = 0.001, 0.01, 0.1 and 1 mL/min), expressed as Ca (logCa = −8.68, −7.68, −6.68 and −5.68), and three different Swi (Swi = 0%, 39.34% and 62.98%). The NMR amplitude is dependent on pore volumes while the NMR transverse relaxation time (T2) spectrum reflects the characteristics of pore size distribution, which is determined based on a mercury injection (MI) experiment. Using this method, the residual gas distribution was quantified by comparing the T2 spectrum of the sample measured after imbibition with the sample fully saturated by brine before imbibition. The results showed that capillary trapping efficiency increased with increasing Swi, and above 90% of residual gas existed in pores larger than 1 μm in the spontaneous imbibition experiments. The residual gas was trapped in pores by different capillary trapping mechanisms under different Ca, leading to the difference of residual gas distribution. The flow channels were mainly composed of micropores (pore radius, r < 1 μm) and mesopores (r = 1–10 μm) at logCa = −8.68 and −7.68, while of mesopores and macropores (r > 10 μm) at logCa = −5.68. At both Swi= 0% and 39.34%, residual gas distribution in macropores significantly decreased while that in micropores slightly increased with logCa increasing to −6.68 and −5.68, respectively.

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