Modeling Cycle-to-Cycle Variations in 0-D/1-D Simulation by Means of Combustion Model Parameter Perturbations based on Statistics of Cycle-Resolved Data

Paper #:
  • 2013-01-1314

Published:
  • 2013-04-08
DOI:
  • 10.4271/2013-01-1314
Citation:
Vitek, O., Macek, J., Poetsch, C., and Tatschl, R., "Modeling Cycle-to-Cycle Variations in 0-D/1-D Simulation by Means of Combustion Model Parameter Perturbations based on Statistics of Cycle-Resolved Data," SAE Int. J. Engines 6(2):1075-1098, 2013, doi:10.4271/2013-01-1314.
Pages:
24
Abstract:
The presented paper deals with a methodology to model cycle-to-cycle variations (CCV) in 0-D/1-D simulation tools. This is achieved by introducing perturbations of combustion model parameters. To enable that, crank angle resolved data of individual cycles (pressure traces) have to be available for a reasonable number of engine cycles. Either experimental data or 3-D CFD results can be applied. In the presented work, experimental data of a single-cylinder research engine were considered while predicted LES 3-D CFD results will be tested in the future. Different engine operating points were selected - both stable ones (low CCV) and unstable ones (high CCV).The proposed methodology consists of two major steps. First, individual cycle data have to be matched with the 0-D/1-D model, i.e., combustion model parameters are varied to achieve the best possible match of pressure traces - an automated optimization approach is applied to achieve that. Second, the combustion model parameters (obtained in previous step) are statistically evaluated to obtain PDFs and cross-correlations. Then such information is imposed to the 0-D/1-D tool by means of both PDFs and cross-correlations of combustion model parameters to mimic pressure traces CCV.Statistics of different engine operating points are compared. The main conclusion is that the CCV effects are overestimated if cross-correlations are not taken into account. It was confirmed that turbulence properties and initial flame kernel development are the dominant factors in terms of CCV. These factors are not independent nor random - they seem to be correlated.
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