Comparing the Exergy Destruction of Methanol and Gasoline in Reactivity Controlled Compression Ignition (RCCI) Engine

2017 ◽  
Author(s):  
Yaopeng Li ◽  
Ming Jia ◽  
Yachao Chang ◽  
Guangfu Xu
Keyword(s):  
Compression Ignition ◽  
Exergy Destruction ◽  
Reactivity Controlled Compression Ignition

scholarly journals Effect of Syngas Composition on the Combustion and Emissions Characteristics of a Syngas/Diesel RCCI Engine

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 212 ◽  
Author(s):  
Navid Kousheshi ◽  
Mortaza Yari ◽  
Amin Paykani ◽  
Ali Saberi Mehr ◽  
German F. de la Fuente
Keyword(s):  
Carbon Monoxide ◽  
Combustion Temperature ◽  
Nox Reduction ◽  
Combustion Process ◽  
Compression Ignition ◽  
Exergy Destruction ◽  
Reactivity Controlled Compression Ignition ◽  
Constant Energy ◽  
Gasification Process ◽  
The Impact

Reactivity controlled compression ignition (RCCI) strategy uses two different fuels with different reactivities which provides more control over the combustion process and has the potential to dramatically lower combustion temperature and NOX and PM emissions. The objective of the present study is to numerically investigate the impact of syngas composition on the combustion and emissions characteristics of an RCCI engine operating with syngas/diesel at constant energy per cycle. For this purpose, different syngas compositions produced through gasification process have been chosen for comparison with the simulated syngas (mixture of hydrogen and carbon monoxide). The results obtained indicate that using syngas results in more soot, CO and UHC emissions compared with simulated syngas. Even though more NOX reduction can be achieved while operating with syngas, the engine could suffer from poor combustion and misfire at low loads due to the presence of nitrogen in the mixture. In terms of exergy, both syngas mixtures lead to more exergy destruction by the increase of syngas substitution. Nevertheless, the magnitude of exergy destruction for simulated syngas is less than the normal syngas.

Potential of hydrogen/diesel reactivity controlled compression ignition (RCCI) combustion from the perspective of the second law of thermodynamics

2021 ◽  
pp. 146808742110601
Author(s):  
Ming Jia ◽  
Jinpeng Bai ◽  
Huiquan Duan ◽  
Yaopeng Li ◽  
Yikang Cai ◽  
...  
Keyword(s):  
Combustion Temperature ◽  
Initial Pressure ◽  
Second Law Of Thermodynamics ◽  
Chemical Mechanism ◽  
Energy Ratio ◽  
Second Law ◽  
Compression Ignition ◽  
Exergy Destruction ◽  
Reactivity Controlled Compression Ignition ◽  
Combustion Duration

The potential of reactivity controlled compression ignition (RCCI) combustion fueled with hydrogen and diesel (i.e. hydrogen/diesel RCCI) was evaluated using multi-dimensional simulations embedded with a reduced chemical mechanism. In hydrogen/diesel RCCI, the premixed hydrogen is ignited by the diesel, which is directly injected into the cylinder well before the top dead center. To investigate the potential benefits of hydrogen/diesel RCCI, its combustion characteristics were compared with that of gasoline/diesel RCCI from the perspective of the second law of thermodynamics. Meanwhile, the impacts of premixed energy ratio and initial pressure on the exergy distribution for hydrogen/diesel RCCI were explored. The results show that hydrogen/diesel RCCI has an advantage over gasoline/diesel RCCI in the reduction of exergy destruction due to higher combustion temperature, shorter combustion duration, and the distinctive oxidation pathways between hydrogen and gasoline. A higher proportion of exergy output work can be achieved for hydrogen/diesel RCCI under the conditions with the same total input energy and 50% heat release (CA50) point. Moreover, a larger premixed energy ratio (i.e. larger hydrogen proportion) is helpful to elevate exergy output work and reduce exergy destruction owing to higher combustion temperature and the undergoing oxidation pathways of hydrogen with less exergy destruction. A higher initial pressure yields raised exergy destruction because of lower combustion temperature and longer combustion duration, but exergy output work is increased owing to the significantly reduced exergy transfer through heat transfer.

Effect of nitrogen and hydrogen addition on performance and emissions in reactivity controlled compression ignition

Fuel ◽  
2021 ◽  
Vol 292 ◽  
pp. 120330
Author(s):  
Sohayb Bahrami ◽  
Kamran Poorghasemi ◽  
Hamit Solmaz ◽  
Alper Calam ◽  
Duygu İpci
Keyword(s):  
Compression Ignition ◽  
Reactivity Controlled Compression Ignition ◽  
Hydrogen Addition ◽  
Performance And Emissions

scholarly journals Effect of Injection Timing and Injection Duration of Manifold Injected Fuels in Reactivity Controlled Compression Ignition Engine Operated with Renewable Fuels

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4621
Author(s):  
P. A. Harari ◽  
N. R. Banapurmath ◽  
V. S. Yaliwal ◽  
T. M. Yunus Khan ◽  
Irfan Anjum Badruddin ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Injection Timing ◽  
Compression Ignition ◽  
Cylinder Pressure ◽  
Compression Ignition Engine ◽  
Compressed Natural Gas ◽  
Reactivity Controlled Compression Ignition ◽  
Brake Thermal Efficiency ◽  
Injection Duration ◽  
Fuel Mixtures

In the current work, an effort is made to study the influence of injection timing (IT) and injection duration (ID) of manifold injected fuels (MIF) in the reactivity controlled compression ignition (RCCI) engine. Compressed natural gas (CNG) and compressed biogas (CBG) are used as the MIF along with diesel and blends of Thevetia Peruviana methyl ester (TPME) are used as the direct injected fuels (DIF). The ITs of the MIF that were studied includes 45°ATDC, 50°ATDC, and 55°ATDC. Also, present study includes impact of various IDs of the MIF such as 3, 6, and 9 ms on RCCI mode of combustion. The complete experimental work is conducted at 75% of rated power. The results show that among the different ITs studied, the D+CNG mixture exhibits higher brake thermal efficiency (BTE), about 29.32% is observed at 50° ATDC IT, which is about 1.77, 3.58, 5.56, 7.51, and 8.54% higher than D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. The highest BTE, about 30.25%, is found for the D+CNG fuel combination at 6 ms ID, which is about 1.69, 3.48, 5.32%, 7.24, and 9.16% higher as compared with the D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. At all ITs and IDs, higher emissions of nitric oxide (NOx) along with lower emissions of smoke, carbon monoxide (CO), and hydrocarbon (HC) are found for D+CNG mixture as related to other fuel mixtures. At all ITs and IDs, D+CNG gives higher In-cylinder pressure (ICP) and heat release rate (HRR) as compared with other fuel combinations.

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.

Reactivity-Controlled Compression Ignition Combustion Using Alcohols

2018 ◽  
pp. 9-28 ◽  
Author(s):  
Akhilendra Pratap Singh ◽  
Nikhil Sharma ◽  
Dev Prakash Satsangi ◽  
Vikram Kumar ◽  
Avinash Kumar Agarwal
Keyword(s):  
Compression Ignition ◽  
Reactivity Controlled Compression Ignition ◽  
Compression Ignition Combustion
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