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Title: Parallel methodology to capture cyclic variability in motored engines

Abstract

Numerical prediction of of cycle-to-cycle variability (CCV) in SI engines is extremely challenging for two key reasons: (i) high-fidelity methods such as large eddy simulation (LES) are require to accurately capture the in-cylinder turbulent flowfield, and (ii) CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. In this study, a new methodology is proposed to dissociate this long time-scale problem into several shorter time-scale problems, which can considerably reduce the computational time without sacrificing the fidelity of the simulations. The strategy is to perform multiple single-cycle simulations in parallel by effectively perturbing the simulation parameters such as the initial and boundary conditions. It is shown that by perturbing the initial velocity field effectively based on the intensity of the in-cylinder turbulence, the mean and variance of the in-cylinder flowfield is captured reasonably well. Adding perturbations in the initial pressure field and the boundary pressure improves the predictions. It is shown that this new approach is able to give accurate predictions of the flowfield statistics in less than one-tenth of time required for the conventional approach of simulating consecutive engine cycles.

Authors:
 [1];  [2];  [2];  [1]
  1. Argonne National Laboratory, Lemont, IL, USA
  2. General Motors R&,D, Warren, MI, USA
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technology
OSTI Identifier:
1412703
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Engine Research; Journal Volume: 18; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS

Citation Formats

Ameen, Muhsin M., Yang, Xiaofeng, Kuo, Tang-Wei, and Som, Sibendu. Parallel methodology to capture cyclic variability in motored engines. United States: N. p., 2016. Web. doi:10.1177/1468087416662544.
Ameen, Muhsin M., Yang, Xiaofeng, Kuo, Tang-Wei, & Som, Sibendu. Parallel methodology to capture cyclic variability in motored engines. United States. doi:10.1177/1468087416662544.
Ameen, Muhsin M., Yang, Xiaofeng, Kuo, Tang-Wei, and Som, Sibendu. Thu . "Parallel methodology to capture cyclic variability in motored engines". United States. doi:10.1177/1468087416662544.
@article{osti_1412703,
title = {Parallel methodology to capture cyclic variability in motored engines},
author = {Ameen, Muhsin M. and Yang, Xiaofeng and Kuo, Tang-Wei and Som, Sibendu},
abstractNote = {Numerical prediction of of cycle-to-cycle variability (CCV) in SI engines is extremely challenging for two key reasons: (i) high-fidelity methods such as large eddy simulation (LES) are require to accurately capture the in-cylinder turbulent flowfield, and (ii) CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. In this study, a new methodology is proposed to dissociate this long time-scale problem into several shorter time-scale problems, which can considerably reduce the computational time without sacrificing the fidelity of the simulations. The strategy is to perform multiple single-cycle simulations in parallel by effectively perturbing the simulation parameters such as the initial and boundary conditions. It is shown that by perturbing the initial velocity field effectively based on the intensity of the in-cylinder turbulence, the mean and variance of the in-cylinder flowfield is captured reasonably well. Adding perturbations in the initial pressure field and the boundary pressure improves the predictions. It is shown that this new approach is able to give accurate predictions of the flowfield statistics in less than one-tenth of time required for the conventional approach of simulating consecutive engine cycles.},
doi = {10.1177/1468087416662544},
journal = {International Journal of Engine Research},
number = 4,
volume = 18,
place = {United States},
year = {Thu Jul 28 00:00:00 EDT 2016},
month = {Thu Jul 28 00:00:00 EDT 2016}
}