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

Format:

Conference/Event

Author/Creator:

Pei, Pei, author.

Contributor:

Davis, Michael J.

Longman, Douglas

Lu, Tianfeng

Shan, Ruiqin

Som, Sibendu

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractGlobal 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, id est, 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

Notes:

Vendor supplied data

Publisher Number:

2014-01-1117

Access Restriction:

Restricted for use by site license