Global Sensitivity Analysis of a Diesel Engine Simulation with Multi-Target Functions

Paper #:
  • 2014-01-1117

Published:
  • 2014-04-01
DOI:
  • 10.4271/2014-01-1117
Citation:
Pei, Y., Shan, R., Som, S., Lu, T. et al., "Global Sensitivity Analysis of a Diesel Engine Simulation with Multi-Target Functions," SAE Technical Paper 2014-01-1117, 2014, https://doi.org/10.4271/2014-01-1117.
Pages:
12
Abstract:
Global Sensitivity Analysis (GSA) is conducted for a diesel engine simulation to understand the sensitivities of various modeling constants and boundary conditions in a global manner with regards to multi-target functions such as liquid length, ignition delays, combustion phasing, and emissions. The traditional local sensitivity analysis approach, which involves sequential perturbation of model constants, does not provide a complete picture since all the parameters can be uncertain. However, this approach has been studied extensively and is advantageous from a computational point of view. The GSA simultaneously incorporates the uncertainty information for all the relevant boundary conditions, modeling constants, and other simulation parameters. A global analysis is particularly useful to address the important parameters in a model where the response of the targets to the values of the variables is highly non-linear.The study represents the first demonstration of the GSA for engine simulations. The baseline is a three-dimensional closed-cycle engine simulation in a 60 degree sector mesh under moderate speed-load conditions. The baseline set-up is able to capture performance and emission trends very well compared to the experiments which were performed in a single-cylinder heavy-duty Caterpillar test engine. The study first quantifies the uncertainties for key model parameters, initial and boundary conditions, i.e., a total of more than 30 parameters. 100 simulations were run by simultaneously varying the above parameters, and the multiple targets are calculated. The GSA is then applied as a screening method to highlight those parameters whose accuracy and adjustments are most likely to influence the predictions of a computational model. The parameters with high sensitivities with regards to multi-target functions are identified and a detailed analysis of the important parameters is presented to different target functions.
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