Abstract
The prospect of analysis-driven precalibration of a modern diesel engine is extremely valuable in order to significantly reduce hardware investments and accelerate engine designs compliant with stricter fuel economy regulations. Advanced modeling tools, such as CFD, are often used with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. However, the effectiveness of CFD simulation tools for in-cylinder engine combustion is often compromised by the complexity, accuracy, and computational overhead of detailed chemical kinetics necessary for combustion calculations. The standard approach has been to use skeletal kinetic mechanisms (∼50 species), which consume acceptable computational time but with degraded accuracy. In this work, a comprehensive demonstration and validation of the analytical precalibration process is presented for a passenger car diesel engine using CFD simulations and a graphical processing unit (GPU)-based chemical kinetics solver (Zero-RK, developed at Lawrence Livermore National Laboratory, Livermore, CA) on high performance computing resources to enable the use of detailed kinetic mechanisms. Diesel engine combustion computations have been conducted over 600 operating points spanning invehicle speed-load map, using massively parallel ensemble simulation sets on the Titan supercomputer located at the Oak Ridge Leadership Computing Facility. The results with different mesh resolutions have been analyzed to compare differences in combustion and emissions (NOx, carbon monoxide CO, unburned hydrocarbons (UHC), and smoke) with actual engine measurements. The results show improved agreement in combustion and NOx predictions with a large n-heptane mechanism consisting of 144 species and 900 reactions with refined mesh resolution; however, agreement in CO, UHC, and smoke remains a challenge.
| Original language | English |
|---|---|
| Article number | 102802 |
| Journal | Journal of Engineering for Gas Turbines and Power |
| Volume | 140 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1 2018 |
Funding
The authors would like to thank the General Motors at Turin Design Center for providing the multicylinder engine dataset. The authors would like to thank Jon Povich, Convergent Science, Inc., for technical support. Portions of this work were supported by the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy under the Vehicle Technology Office’s (VTO) Advanced Combustion Engines Program and performed at Oak Ridge National Laboratory (ORNL) by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 and at Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. The authors gratefully acknowledge the support and direction of Leo Breton and Gurpreet Singh at DOE’s VTO. Portions of this work used resources of the Oak Ridge Leadership Computing Facility at ORNL, which is supported by the DOE Office of Science under Contract No. DE-AC05-00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.2