Browse Publications Technical Papers 2020-01-2079
2020-09-15

Toward Predictive Combustion Modeling of CNG SI Engines in 1D Simulation Tools 2020-01-2079

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional fuel to CNG.
To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts.
In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on. The present work is focused on the improvement of the combustion model of natural gas spark ignition engines suitable for 1D simulation. To this aim, an extensive experimental campaign of a SI engine, customized for CNG use, is carried out at various speeds and loads, in which global engine performance and cylinder pressure traces are recorded and analyzed. Subsequently, a 1D model of the tested engine is implemented in a commercial tool and integrated with “in-house developed” sub-models for the description of in-cylinder phenomena, such as combustion, turbulence and heat transfer.
The proposed numerical approach, extensively validated in the past for gasoline fueled engines, shows here the ability to reproduce with good accuracy the performance of a heavy-duty CNG fueled engine, too. In particular, the in-cylinder pressure traces, burn rates and main performance parameters, are well predicted in its whole operating plane, without requiring a case-dependent tuning.

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