Adaptive Injection Strategies (AIS) for Ultra-Low Emissions Diesel Engines

Sun, Yong; Reitz, Rolf D.

doi:10.4271/2008-01-0058

Cited by 30 publications

(13 citation statements)

References 33 publications

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“…PM measurements were performed with an AVL model 415s smoke meter. PM measurements of filter smoke number (FSN), mass per volume (mg/m^3) and specific emissions (g/kWh) were related with the factory AVL calibration and averaged between 5 samples of a 2 l † Sun et al [15] showed that for very early injection timings (near BDC), liquid impingement on the cylinder liner was significantly reduced when a low-pressure, narrow angle, GDI-type injector was used rather than a conventional, high-pressure, wide-angle diesel injector.…”

Section: Injection Systemmentioning

confidence: 99%

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Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending

Kokjohn,

Hanson,

Splitter

et al. 2009

SAE Int. J. Engines

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552

223

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This study investigates the potential of controlling premixed charge compression ignition (PCCI and HCCI) combustion strategies by varying fuel reactivity. Incylinder fuel blending using port fuel injection of gasoline and early cycle direct injection of diesel fuel was used for combustion phasing control at both high and low engine loads and was also effective to control the rate of pressure rise. The first part of the study used the KIVA-CHEMKIN code and a reduced primary reference fuel (PRF) mechanism to suggest optimized fuel blends and EGR combinations for HCCI operation at two engine loads (6 and 11 bar net IMEP). It was found that the minimum fuel consumption could not be achieved using either neat diesel fuel or neat gasoline alone, and that the optimal fuel reactivity required decreased with increasing load. For example, at 11 bar net IMEP, the optimum fuel blend and EGR rate for HCCI operation was found to be PRF 80 and 50%, respectively. Engine experiments using a dual-fuel PCCI strategy with port fuel injection of gasoline and early cycle multiple injections of diesel fuel with a conventional diesel injector (i.e., wide angle and large nozzle hole) were performed. The experimental results confirmed that an extension of the PCCI operating regime is possible when optimized fuel blends are used. At the 11 bar operating point, NOx and soot were ~0.01 g/kW-hr and ~0.008 g/kW-hr, respectively. That is, US 2010 heavy duty emissions regulations are easily met without aftertreatment while achieving 50% thermal efficiency.

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Section: Injection Systemmentioning

confidence: 99%

“…/ 0.42 RON CN (15) Gasoline was delivered using the port fuel injector of Table 2 and diesel fuel was directly injected at 140°BTDC using the GDI injector of Table 3. Figure 8 shows measured net ISFC contours over the PRF and EGR sweep.…”

mentioning

confidence: 99%

Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending

Kokjohn,

Hanson,

Splitter

et al. 2009

SAE Int. J. Engines

Self Cite

552

223

View full text Add to dashboard Cite

show abstract

“…NOx is predicted using a 12-reaction NOx mechanism based on the Gas Research Institute (GRI) NO mechanism. 38 The spray model uses the Lagrangian-Drop–Eulerian-Fluid (LDEF) approach. In order to reduce the grid size dependency of the LDEF spray model and allow accurate spray simulation in the near nozzle region, the Gasjet model of Abani et al 39,40 is used to model the relative velocity between the droplets and gas phase in the near nozzle region.…”

Section: Methodsmentioning

confidence: 99%

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

Kavuri

Kokjohn

2018

International Journal of Engine Research

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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.

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“…16 NOx is predicted using a 12-reaction NOx mechanism based on the Gas Research Institute (GRI) NO mechanism. 17 The two-step phenomenological soot model based on the approach of Hiroyasu and Kadota 18 predicts soot formation and oxidation. The soot model uses acetylene as an inception species.…”

Section: Methodsmentioning

confidence: 99%

Blending the benefits of reactivity controlled compression ignition and gasoline compression ignition combustion using an adaptive fuel injection system

Kavuri

Kokjohn

Klos

et al. 2016

International Journal of Engine Research

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Computational optimizations of dual-fuel reactivity controlled compression ignition combustion and gasoline compression ignition combustion were performed using a novel adaptive dual-fuel injector capable of direct injecting both gasoline and diesel fuel in a single cycle. Optimization used the Engine Research Center KIVA code coupled with a multiobjective genetic algorithm. Model validation was performed by comparing simulation results to conventional diesel, reactivity controlled compression ignition, and gasoline compression ignition combustion, and the validated model was used to develop an optimum reactivity controlled compression ignition–gasoline compression ignition combustion strategy. The reactivity controlled compression ignition optimization results showed that by direct injecting gasoline and diesel fuel, the gasoline quantity can be held at a high percentage across the range of loads considered. In this study, the mode weighted gasoline percentage was 91%. At the lightest load point, direct injecting the gasoline gave optimum results, whereas for the other load points, premixing the gasoline yielded the optimum results. The optimized results were compared with conventional diesel combustion, and it was seen that reactivity controlled compression ignition combustion gives a cycle-averaged improvement of 33% in gross indicated efficiency over conventional diesel combustion. The cycle-averaged NOx and soot emissions were reduced by 95% and 75%, respectively. To demonstrate operation over the entire operating map, an optimization was performed at a high-speed–high-load (16 bar, 2500 r/min) condition. Optimization results showed that a gross indicated efficiency of 46.4% with near zero NOx and soot emissions could be achieved using gasoline compression ignition at this load point.

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Adaptive Injection Strategies (AIS) for Ultra-Low Emissions Diesel Engines

Cited by 30 publications

References 33 publications

Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending

Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending

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

Blending the benefits of reactivity controlled compression ignition and gasoline compression ignition combustion using an adaptive fuel injection system

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