Experimental and computational investigation of soot production from a premixed compression ignition engine using a load extension injection

To improve the trade-off between thermal efficiency and peak heat release rate (HRR) of partially premixed combustion (PPC) and the combustion efficiency of reactivity-controlled compression ignition (RCCI), the combustion mode with premixed high-reactivity fuel and direct-injection (DI) low-reactivity fuel, called RCCI with reverse reactivity stratification (R-RCCI), was explored at low loads in a light-duty diesel engine in this study. Compared with diesel, polyoxymethylene dimethyl ethers (PODEn) has better volatility, which is beneficial for the formation of premixed charge, so it was used as the premixed high-reactivity fuel for R-RCCI in this work. The gasoline and P20G80 (PODEn/gasoline blends with the volume fraction of 20%/80%) were respectively applied as the DI low-reactivity fuel. By investigating the combustion characteristics of R-RCCI, it is found that R-RCCI can break the trade-off between combustion efficiency and nitrogen oxides (NOx) emissions. This is because the combustion efficiency of R-RCCI is dominated by the spray location of the DI fuel rather than the 50% burn point (CA50). As the start of injection (SOI) timing is retarded, the fuel injected within the piston bowl increases, and combustion efficiency, as well as indicated thermal efficiency (ITE), is considerably promoted. Meanwhile, CA50 progressively retards with delayed SOI timing, which effectively reduces NOx emissions. The soot emissions of R-RCCI are also extremely low. The maximum ITE of PODEn/P20G80 R-RCCI is significantly higher than that of PODEn/gasoline R-RCCI. This occurs because the higher reactivity of P20G80 can reduce the sensitivity of CA50 to SOI timing and improve combustion stability, so a more delayed SOI timing is allowed to improve ITE. With the same engine configurations, R-RCCI can reduce peak pressure rise rate and improve combustion stability, while enhancing combustion efficiency and ITE compared with RCCI at the low-load conditions tested in this study.

Section: Resultssupporting

confidence: 90%

Potential of reactivity-controlled compression ignition with reverse reactivity stratification (R-RCCI) fueled with gasoline and polyoxymethylene dimethyl ethers (PODE_n)

Duan

Jia

Bai

et al. 2021

“…That is, the SOI timing of the load-extension injection is as advanced as possible (upon the completion of the primary heat release), to maximize the efficiency while meeting the soot constraint. Similar results were also demonstrated in Kavuri and colleagues 35,45 where a detailed analysis on the constraining factors was presented while investigating mixed-mode combustion strategies as a pathway to enable stable high-load operation at reduced EGR rates. In summary, based on the results shown in this section, using the GPR model to perform the stability analysis within the GA optimization seems to be a potential approach to achieve stable and reliable optima from the GA optimizations.…”

Section: Stability Analysis In Ga Optimization Using Gprsupporting

confidence: 75%

“…33 The reaction mechanism used considered polycyclic aromatic hydrocarbons chemical kinetics up to pyrene. The two-step phenomenological soot model based on the approach of Hiroyasu and Kadota 34 was modified to include soot inception through pyrene rather than fuel or acetylene to describe soot formation more accurately, as was identified in an earlier study by Kavuri et al 35 The rate of change of soot mass within a computational cell, M · s , is given by competition between the soot formation rate, M · sf , and the soot oxidation rate, M · so …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the potential of machine learning in reducing the computational time/expense and improving the reliability of engine optimization studies

Kavuri

Kokjohn

2018

Self Cite

Past research has shown that multidimensional computational fluid dynamics modeling in combination with a genetic algorithm method is an effective approach for optimizing internal combustion engine design. However, optimization studies performed with a detailed computational fluid dynamics model are time intensive, which limits the practical application of this approach. This study addresses this issue by using a machine learning approach called Gaussian process regression in combination with computational fluid dynamics modeling to reduce the computational optimization time. An approach was proposed where the Gaussian process regression model could be used instead of the computational fluid dynamics model to predict the outputs of the genetic algorithm optimization. In this approach, for every nth generation of the genetic algorithm, the data from the previous n − 1 generations was used to train the Gaussian process regression model. The approach was tested on an engine optimization study with five input parameters. When the genetic algorithm was run solely with computational fluid dynamics, the optimization took 50 days to complete. In comparison with the computational fluid dynamics and Gaussian process regression approach, the computational time was reduced by 62%, and the optimization was completed in 19 days using the same amount of computational resources. Additional parametric studies were performed to investigate the impact of genetic algorithm + Gaussian process regression parameters. Results showed that either reducing the initial dataset size or relaxing the error criterion resulted in increased Gaussian process regression evaluations within the genetic algorithm. However, relaxing the error criterion was found to impact the model predictions negatively. The initial dataset size was found to have a negligible impact on the final optimum design. Finally, the potential of machine learning in further improving the optimization process was explored by using the Gaussian process regression model to check for the robustness of the designs to operating parameter variations during the optimization. The genetic algorithm was repeated with the modified procedure and it was shown that adding the stability check resulted in a different, more reliable and stable optimum solution.

“…140 A premixed charge of gasoline and n-heptane is found to be very effective to control combustion phasing and load extension is achieved by the injection of gasoline near TDC. 141…”

Section: Fuel Effects In Diesel Ltcmentioning

confidence: 99%

Prospective fuels for diesel low temperature combustion engine applications: A critical review

Krishnasamy

Gupta

Reitz

2020

Low Temperature Combustion (LTC) strategies are most promising to simultaneously reduce oxides of nitrogen (NOx) and soot emissions from diesel engines along with offering higher thermal efficiency. Commercial wide spread implementation of diesel LTC strategies requires several challenges to be addressed, including lack of precise ignition timing control, widening the narrow operating load ranges and reducing high unburned fuel emissions. These challenges can be addressed through modifications in the engine or fuel design or both. The timing and rate of combustion in several LTC strategies are controlled primarily by the chemical kinetics of the fuel. Since, diesel fuel reactivity and volatility are tailor-made to perform well under conventional diesel combustion conditions, its application in LTC poses several problems, as highlighted in this paper. Hence, it is important to identify suitable alternative fuels for the different diesel LTC strategies. The published literature on LTC over the past 25 years is critically analyzed to discuss the evolution of the different diesel LTC strategies, their operability limits, the challenges and the controlling parameters for each strategy. This is followed by in-depth analysis of the role of the fuel and the fuel requirements for each strategy. Further, the importance of adopting a hybrid surrogate modeling approach to enable numerical simulation of diesel LTC is highlighted. A novel attempt of relating various diesel low temperature combustion (LTC) strategies based on the approach followed to achieve positive ignition dwell through different injection strategies, utilizing high exhaust gas recirculation (EGR), and dual fuels is presented. The need for replacing diesel with alternative liquid fuels in LTC strategies is presented by highlighting the fundamental problems associated with diesel fuel characteristics. The review concludes by suggesting potential alternative fuels for various diesel LTC strategies and provides directions for future work to address the challenges facing compression ignition LTC operation.