Chemical effect of CO2 on the laminar flame speeds of oxy-methane mixtures in the condition of various equivalence ratios and oxygen concentrations

2016 ◽  
Vol 41 (33) ◽  
pp. 15068-15077 ◽  
Author(s):  
Xianzhong Hu ◽  
Qingbo Yu ◽  
Junxiang Liu
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weijian Zhou ◽  
Song Zhou ◽  
Hongyuan Xi ◽  
Majed Shreka ◽  
Zhao Zhang

The effect of in-cylinder fuel reforming on an n-heptane homogenous charge compression ignition engine has been studied. A dedicated cylinder without a complex control system is proposed for fuel enrichment reforming, which can provide part of the power for the engine. The effects of different reforming species on engine performance and chemical reaction have been simulated by a numerical study. By comparing the combustion characteristics of n-heptane with different equivalence ratios in the reformer cylinder, the optimal n-heptane equivalence ratio has been determined. The enrichment of n-heptane produces sufficient hydrogen (H2) and carbon monoxide (CO), while the hydrocarbon content of the reforming species was low. It was found that the addition of reforming species retards the combustion phase of n-heptane, thereby providing a means of controlling engine performance. In addition, the laminar flame speed and the adiabatic flame temperature of n-heptane increased by adding H2 and CO. Fuel reforming reduced the emission of ethylene, propyne, allene, propylene, butadiene, and nitrogen oxide, but it increased the emissions of acetylene and CO. Moreover, chemical, dilution, and thermodynamic effects of the reforming gas have been studied. The results showed that the chemical effect of the reforming species was less significant than the dilution and thermodynamic effects. These simulation results showed that in-cylinder fuel reforming can effectively improve engine performance and thereby reduce emissions.


2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Chao Xu ◽  
Pinaki Pal ◽  
Xiao Ren ◽  
Magnus Sjöberg ◽  
Noah Van Dam ◽  
...  

Abstract In this study, lean mixed-mode combustion is numerically investigated using computational fluid dynamics (CFD) in a spark-ignition engine. A new E30 fuel surrogate is developed using a neural network model with matched octane numbers. A skeletal mechanism is also developed by automated mechanism reduction and by incorporating a NOx submechanism. A hybrid approach that couples the G-equation model and the well-stirred reactor model is employed for turbulent combustion modeling. The developed CFD model is shown to well predict pressure and apparent heat release rate (AHRR) traces compared with experiment. Two types of combustion cycles (deflagration-only and mixed-mode cycles) are observed. The mixed-mode cycles feature early flame propagation and subsequent end-gas auto-ignition, leading to two distinctive AHRR peaks. The validated CFD model is then employed to investigate the effects of NOx chemistry. The NOx chemistry is found to promote auto-ignition through the residual gas, while the deflagration phase remains largely unaffected. Sensitivity analysis is finally performed to understand effects of fuel properties, including heat of vaporization (HoV) and laminar flame speed (SL). An increased HoV tends to suppress auto-ignition through charge cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased SL is found to significantly promote both flame propagation and end-gas auto-ignition. The promoting effect of SL on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end-gas.


2020 ◽  
Vol 65 (6) ◽  
pp. 529-537
Author(s):  
Domnina RAZUS ◽  
◽  
Maria MITU ◽  
Venera GIURCAN ◽  
Codina MOVILEANU ◽  
...  

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>


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