skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

Abstract

Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. First, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the in-cylinder turbulent flow field both spatially and temporally. Second, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (2017, "Parallel Methodology to Capture Cyclic Variability in Motored Engines,"Int. J. Engine Res., 18(4), pp. 366-377.) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter time-scale problems. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flow field was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) spark ignition (SI) engine. The predictions from this approach are also shown to be similar to the consecutive LES cycles. It is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn raterelated parametersmore » with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Argonne National Laboratory,9700 S Cass Avenue,Lemont, IL 60439e-mail: mameen@anl.gov
  2. Politecnico di Torino,c.so Duca degli Abruzzi, 24,Torino 10129, Italye-mail: mohsen.mirzaeian@polito.it
  3. Politecnico di Torino,c.so Duca degli Abruzzi, 24,Torino 10129, Italye-mail: federico.millo@polito.it
  4. Argonne National Laboratory,9700 S Cass Avenue,Lemont, IL 60439e-mail: ssom@anl.gov
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; USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Bioenergy Technologies Office (BETO)
OSTI Identifier:
1461465
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Energy Resources Technology
Additional Journal Information:
Journal Volume: 140; Journal Issue: 5; Journal ID: ISSN 0195-0738
Publisher:
ASME
Country of Publication:
United States
Language:
English

Citation Formats

Ameen, Muhsin M., Mirzaeian, Mohsen, Millo, Federico, and Som, Sibendu. Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach. United States: N. p., 2018. Web. doi:10.1115/1.4039549.
Ameen, Muhsin M., Mirzaeian, Mohsen, Millo, Federico, & Som, Sibendu. Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach. United States. doi:10.1115/1.4039549.
Ameen, Muhsin M., Mirzaeian, Mohsen, Millo, Federico, and Som, Sibendu. Thu . "Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach". United States. doi:10.1115/1.4039549.
@article{osti_1461465,
title = {Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach},
author = {Ameen, Muhsin M. and Mirzaeian, Mohsen and Millo, Federico and Som, Sibendu},
abstractNote = {Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. First, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the in-cylinder turbulent flow field both spatially and temporally. Second, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (2017, "Parallel Methodology to Capture Cyclic Variability in Motored Engines,"Int. J. Engine Res., 18(4), pp. 366-377.) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter time-scale problems. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flow field was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) spark ignition (SI) engine. The predictions from this approach are also shown to be similar to the consecutive LES cycles. It is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn raterelated parameters with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.},
doi = {10.1115/1.4039549},
journal = {Journal of Energy Resources Technology},
issn = {0195-0738},
number = 5,
volume = 140,
place = {United States},
year = {2018},
month = {3}
}