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A Simulation Research on Emission Control Technology of Low-Speed Two-Stroke Diesel Engine Based on EGR and Miller Cycle
Technical Paper
2019-01-0945
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper investigates the influences of EGR and Miller cycle on NOx emission of a heavy-duty two-stroke diesel engine. The NOx emission is strictly restricted by the IMO Tier III Emission Regulations, resulting in an insufficient application of the single emission reduction technology to meet the emission requirements. It is asserted that EGR is the most effective manner to reduce NOx emission, but the fuel consumption increases simultaneously. In consideration of emission reduction with fuel economy, EGR and Miller cycle were combined and studied in this paper. Parameters like in-cylinder pressure, in-cylinder temperature, mass in the chamber, emission (NOx and soot) and fuel consumption rate were investigated based on a single-cylinder 3D model. The wet condition that happens in the engine application was considered in the model development process. The model was validated and compared with the experimental data. The simulation results show the "trade-off" relationship between NOx and soot under EGR, as well as the performance of reducing NOx in different load. This paper, subsequently, used Miller cycle (achieve by delay the closing timing of the exhaust valve with intake boosting) to optimize the fuel consumption rate base on the EGR results. Finally, the combustion conditions under different EGR ratio and different Miller cycle condition were reflected by in-cylinder pressure, the contours of in-cylinder temperatures and the generating area of NOx. According to the simulation results of EGR and Miller cycle, the optimum scheme was giving out to satisfy the IMO Tier Three with better fuel economy performance.
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Zhu, Z., Liang, X., Wang, Y., and Liu, B., "A Simulation Research on Emission Control Technology of Low-Speed Two-Stroke Diesel Engine Based on EGR and Miller Cycle," SAE Technical Paper 2019-01-0945, 2019, https://doi.org/10.4271/2019-01-0945.Data Sets - Support Documents
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References
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