Characterizing Gaseous Fuels for Their Knock Resistance based on the Chemical and Physical Properties of the Fuel

Paper #:
  • 2015-01-9077

  • 2016-03-14
  • 10.4271/2015-01-9077
Gersen, S., Essen, M., Levinsky, H., and Dijk, G., "Characterizing Gaseous Fuels for Their Knock Resistance based on the Chemical and Physical Properties of the Fuel," SAE Int. J. Fuels Lubr. 9(1):1-13, 2016, doi:10.4271/2015-01-9077.
A method is described to characterize the effects of changes in the composition of gaseous fuels on engine knock by computing the autoignition process during the compression and burn periods of the engine cycle. To account for the effects of fuel composition on the in-cylinder pressure and temperature history relevant for knocking, changes in heat capacity of the air-fuel mixture and in the phasing of the combustion process are also incorporated in the method. Comparison between pressure profiles measured in a lean-burn, high-speed medium-BMEP gas engine and the calculated pressure profiles at non-knocking conditions shows that the method accurately computes the in-cylinder pressure history when varying the fuel composition. To characterize gaseous fuels for their resistance to knock, a propane-based scale (Propane Knock Index, PKI) is reported in this study. On this scale, the knock resistance for a given gaseous fuel mixture is expressed as an equivalent fraction of propane in methane under identical engine conditions. The chemical mechanism used to compute the autoignition delay time is optimized based on RCM measurements performed at engine conditions. After optimization, the knock resistance as measured by the knock-limited spark timing (KLST) and calculated using PKI was found to agree within the uncertainty of the measurements (± 0.75 °CA) for all fuels studied, including hydrocarbon mixtures representative for liquefied natural gas (LNG) and natural gas/H2/CO mixtures. In contrast to the method reported here, comparison of KLST for the range of gases studied with methane numbers calculated using the AVL and the MWM methods points to shortcomings in these yardsticks.
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