Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations
Abstract
In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine usingmore »
- Authors:
-
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Convergent Science Inc., Madison, WI (United States)
- Sandia National Laboratory (SNL-CA), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1960145
- Grant/Contract Number:
- AC02-06CH11357; NA0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Science and Technology for Energy Transition
- Additional Journal Information:
- Journal Volume: 77; Journal ID: ISSN 2804-7699
- Publisher:
- EDP Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS; advanced ignition modeling; ignition-combustion coupling; internal combustion engines; spark ignition
Citation Formats
Kazmouz, Samuel J., Scarcelli, Riccardo, Cheng, Zhen, Dai, Meizhong, Pomraning, Eric, Senecal, Peter K., and Sjöberg, Magnus. Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations. United States: N. p., 2022.
Web. doi:10.2516/stet/2022009.
Kazmouz, Samuel J., Scarcelli, Riccardo, Cheng, Zhen, Dai, Meizhong, Pomraning, Eric, Senecal, Peter K., & Sjöberg, Magnus. Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations. United States. https://doi.org/10.2516/stet/2022009
Kazmouz, Samuel J., Scarcelli, Riccardo, Cheng, Zhen, Dai, Meizhong, Pomraning, Eric, Senecal, Peter K., and Sjöberg, Magnus. Tue .
"Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations". United States. https://doi.org/10.2516/stet/2022009. https://www.osti.gov/servlets/purl/1960145.
@article{osti_1960145,
title = {Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations},
author = {Kazmouz, Samuel J. and Scarcelli, Riccardo and Cheng, Zhen and Dai, Meizhong and Pomraning, Eric and Senecal, Peter K. and Sjöberg, Magnus},
abstractNote = {In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.},
doi = {10.2516/stet/2022009},
journal = {Science and Technology for Energy Transition},
number = ,
volume = 77,
place = {United States},
year = {Tue May 31 00:00:00 EDT 2022},
month = {Tue May 31 00:00:00 EDT 2022}
}
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