Simulation of Intake Manifold Water Injection in a Heavy Duty Natural Gas Engine for Performance and Emissions Enhancement 2018-01-1653
The present work discusses the effects of intake manifold water injection in a six-cylinder heavy duty natural gas (NG) engine through one-dimensional simulation. The numerical study was carried out based on GT-Power under different engine working conditions. The established simulation model was firstly calibrated in detail through the whole engine speed sweep under full load conditions before the model of intake manifold water injector was involved, and the calibration was based on experimental data. The intake manifold water injection mass was controlled through adjustment of intake water/gas (water/natural gas) ratio, a water/gas ratio swept from 0 to 4 was selected to investigate the effects of intake manifold water injection on engine performance and emissions characteristics. On the other hand, the enhancement potential of intake manifold water injection in heavy duty NG engine under lean and stoichiometric condition was also investigated by the alteration of air-fuel ratio. The calculation results demonstrated that in considering maximum performance enhancement strategy preferentially, the engine performance characteristics was increased around 3% under lean condition while 7%-10% at stoichiometric condition, with a drastic NOX emissions reduction capability around 70%-80%. When took minimum NOX emissions as a primary consideration for water injection strategy, over 90% of NOX emissions at lean condition and 80% of NOX emissions at stoichiometric condition could be eliminated with performance characteristics and BSFC deterioration less than 10%. To achieve optimized engine performance and emissions characteristics simultaneously, the optimum engine control strategy with intake manifold water injection could be attained through an advanced spark timing while maintaining water/gas ratio the same as the minimum NOX emissions strategy.
Zeqi Kang, Zhe Kang, Lang Jiang, Jun Deng, Zhijun Wu, Liguang Li, Heping Liang, Mingyu Shu