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Extension of the Phenomenological 3-Arrhenius Auto-Ignition Model for Six Surrogate Automotive Fuels
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 05, 2016 by SAE International in United States
Citation: Blomberg, C., Mitakos, D., Bardi, M., Boulouchos, K. et al., "Extension of the Phenomenological 3-Arrhenius Auto-Ignition Model for Six Surrogate Automotive Fuels," SAE Int. J. Engines 9(3):1544-1558, 2016, https://doi.org/10.4271/2016-01-0755.
An existing three-stage ignition delay model which has seen successful application to Primary Reference Fuels (PRFs) has been extended to six surrogate fuels which constitute potential candidates for future Homogeneous Charge Compression Ignition (HCCI) engines. The fuels include petroleum-derived and oxygenated components and can be divided into low, intermediate and high cetane number groups. A new methodology to obtain the model parameters is presented which relies jointly on simulation and experimental data: in a first step, constant volume adiabatic reactor simulations using chemical kinetic mechanisms are performed to generate ignition delays for a very wide range of conditions, namely variations in equivalence ratio, Exhaust Gas Recirculation (EGR), pressure and temperature. Based on this “virtual shock tube” data the 3-Arrhenius parameters for low- and high-temperature ignition delay and Negative Temperature Coefficient (NTC) regimes are determined by means of a genetic algorithm. In a second step, the parameterized 3-Arrhenius model is assessed by means of experimental auto-ignition delay data from a Rapid Compression Expansion Machine (RCEM). This data revealed a systematic over prediction of the low and high temperature ignition delays. A refinement of the pre-exponential parameters of the low and high ignition delay terms was carried out resulting in excellent agreement over a large range of operating conditions between model and measurements for all six fuels. The approach proposed in this study hence combines 1) the efficient generation of ‘numerical’ ignition delay data for surrogate fuels at different pressure, temperature, equivalence ratio and EGR levels for which the variations span much larger ranges than typically available from experiments, 2) the rapid parameterization of this dataset by means of genetic algorithms to derive “initial estimates” for the three-stage ignition model parameters, for which in 3) the final values are determined by retuning the pre-exponential parameters of the low and high ignition delay terms based on a wide range of measurements in a RCEM. To conclude the new methodology has successfully extended the application of the 3-Arrhenius model to Toluene Reference Fuels (TRFs), longer-chain alkanes and oxygenated fuels. The efficiency and robustness of the new methodology compared to the traditional usage of exclusively experimental data for model parameterization considerably extends its applicability to auto-ignition modeling for a variety of fuels. This will allow for broader use of such models for different fuels for HCCI engine calibration and control.