4.8 Article

Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition

期刊

APPLIED ENERGY
卷 248, 期 -, 页码 95-103

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.04.098

关键词

Double-Wiebe function; Two-stage combustion; Natural gas; Spark ignition; Lean burn; Diesel geometry

资金

  1. WV Higher Education Policy Commission [HEPC.dsr.18.7]
  2. WVU's Center for Alternative Fuels Engines and Emissions (CAFEE)
  3. WVU's Center for Innovation in Gas Research and Utilization (CIGRU)
  4. MAE department

向作者/读者索取更多资源

The conversion of existing diesel engines to natural-gas spark-ignition operation would reduce the dependence on oil imports, reduce the burden on refining capacity, and increase U.S. energy security. The use of simple combustion models such as the Wiebe function to analyze or predict the combustion process in such retrofitted engines can accelerate and optimize the engine conversion. This study compared the standard single Wiebe function to a double-Wiebe function to investigate if the latter will improve the predicted mass fraction burned and, if yes, which of formats of the duel-Wiebe function in the literature described the best the mass fraction burned in such converted engine. The results showed that while the standard Wiebe function could not predict the mass fraction, a double-Wiebe function (one for the fast burn inside the piston bowl and a second for the slower burning process inside the squish region) predicted with good accuracy the mass fraction burned, heat release rate, in-cylinder pressure, and combustion phasing in such converted engines (similar to 90% reduction in error) for the conditions investigated here, especially if the start of the second Wiebe was delayed. Moreover, such a condition-dependent model as the double-Wiebe model is limited to the operating conditions used for determining the model parameters. However, the model can be developed into a condition-independent model if enough experimental data or CFD simulations are available to find the unique set (or sets) of parameters that minimizes the error at most conditions.

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