0
Research Papers

Implementation of Detailed Chemistry Mechanisms in Engine Simulations

[+] Author and Article Information
Prithwish Kundu

Argonne National Laboratory,
Lemont, IL 60439
e-mail: pkundu@anl.gov

Muhsin M. Ameen

Argonne National Laboratory,
Lemont, IL 60439
e-mail: mameen@anl.gov

Chao Xu

University of Connecticut,
Storrs, CT 06269
e-mail: chao.xu@uconn.edu

Umesh Unnikrishnan

Argonne National Laboratory,
Lemont, IL 60439
e-mail: umesh.aero@gatech.edu

Tianfeng Lu

University of Connecticut,
Storrs, CT 06269
e-mail: tlu@engr.uconn.edu

Sibendu Som

Argonne National Laboratory,
Lemont, IL 60439
e-mail: ssom@anl.gov

Manuscript received July 17, 2018; final manuscript received August 8, 2018; published online October 16, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 141(1), 011026 (Oct 16, 2018) (10 pages) Paper No: GTP-18-1498; doi: 10.1115/1.4041281 History: Received July 17, 2018; Revised August 08, 2018

The stiffness of large chemistry mechanisms has been proved to be a major hurdle toward predictive engine simulations. As a result, detailed chemistry mechanisms with a few thousand species need to be reduced based on target conditions so that they can be accommodated within the available computational resources. The computational cost of simulations typically increases super-linearly with the number of species and reactions. This work aims to bring detailed chemistry mechanisms within the realm of engine simulations by coupling the framework of unsteady flamelets and fast chemistry solvers. A previously developed tabulated flamelet model (TFM) framework for nonpremixed combustion was used in this study. The flamelet solver consists of the traditional operator-splitting scheme with variable coefficient ordinary differential equation (ODE) solver (VODE) and a numerical Jacobian for solving the chemistry. In order to use detailed mechanisms with thousands of species, a new framework with the Livermore solver for ODEs in sparse form (LSODES) chemistry solver and an analytical Jacobian was implemented in this work. Results from 1D simulations show that with the new framework, the computational cost is linearly proportional to the number of species in a given chemistry mechanism. As a result, the new framework is 2–3 orders of magnitude faster than the conventional variable coefficient ODE (VODE) solver for large chemistry mechanisms. This new framework was used to generate unsteady flamelet libraries for n-dodecane using a detailed chemistry mechanism with 2755 species and 11,173 reactions. The engine combustion network (ECN) spray A experiments, which consist of an igniting n-dodecane spray in turbulent, high-pressure engine conditions are simulated using large eddy simulations (LES) coupled with detailed mechanisms. A grid with 0.06 mm minimum cell size and 22 ×106 peak cell count was implemented. The framework is validated across a range of ambient temperatures against ignition delay and liftoff lengths (LOLs). Qualitative results from the simulations were compared against experimental OH and CH2O planar laser-induced fluorescence (PLIF) data. The models are able to capture the spatial and temporal trends in species compared to those observed in the experiments. Quantitative and qualitative comparisons between the predictions of the reduced and detailed mechanisms are presented in detail. The main goal of this study is to demonstrate that detailed reaction mechanisms (∼1000 species) can now be used in engine simulations with a linear increase in computation cost with number of species during the tabulation process and a small increase in the 3D simulation cost.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

McNenly, M. J. , Whitesides, R. A. , and Flowers, D. L. , 2015, “ Faster Solvers for Large Kinetic Mechanisms Using Adaptive Preconditioners,” Proc. Combust. Inst., 35(1), pp. 581–587. [CrossRef]
Xu, C. , Gao, Y. , Ren, Z. , and Lu, T. , 2016, “ A Sparse Stiff Chemistry Solver Based on Dynamic Adaptive Integration for Efficient Combustion Simulations,” Combust. Flame, 172, pp. 183–193. [CrossRef]
Green, W. H. , 2007, “ Predictive Kinetics: A New Approach for the 21st Century,” Adv. Chem. Eng., 32, pp. 1–313. [CrossRef]
Curran, H. J. , Gaffuri, P. , Pitz, W. J. , and Westbrook, C. K. , 1998, “ A Comprehensive Modeling Study of n-Heptane Oxidation,” Combust. Flame, 114(1–2), pp. 149–177. [CrossRef]
Herbinet, O. , Pitz, W. J. , and Westbrook, C. K. , 2008, “ Detailed Chemical Kinetic Oxidation Mechanism for a Biodiesel Surrogate,” Combust. Flame, 154(3), pp. 507–528. [CrossRef]
Pei, Y. , Mehl, M. , Liu, W. , Lu, T. , Pitz, W. J. , and Som, S. , 2015, “ A Multicomponent Blend as a Diesel Fuel Surrogate for Compression Ignition Engine Applications,” ASME J. Eng. Gas Turbines Power, 137(11), p. 111502. [CrossRef]
Sarathy, S. M. , Westbrook, C. K. , Mehl, M. , Pitz, W. J. , Togbe, C. , Dagaut, P. , Wang, H. , Oehlschlaeger, M. A. , Niemann, U. , and Seshadri, K. , 2011, “ Comprehensive Chemical Kinetic Modeling of the Oxidation of 2-Methylalkanes From C 7 to C 20,” Combust. Flame, 158(12), pp. 2338–2357. [CrossRef]
Brown, P. N. , Byrne, G. D. , and Hindmarsh, A. C. , 1989, “ VODE: A Variable-Coefficient ODE Solver,” SIAM J. Sci. Stat. Comput., 10(5), pp. 1038–1051. [CrossRef]
Perini, F. , Galligani, E. , and Reitz, R. D. , 2014, “ A Study of Direct and Krylov Iterative Sparse Solver Techniques to Approach Linear Scaling of the Integration of Chemical Kinetics With Detailed Combustion Mechanisms,” Combust. Flame, 161(5), pp. 1180–1195. [CrossRef]
Lu, T. , and Law, C. K. , 2005, “ A Directed Relation Graph Method for Mechanism Reduction,” Proc. Combust. Inst., 30(1), pp. 1333–1341. [CrossRef]
Pitsch, H. , Barths, H. , and Peters, N. , 1996, “ Three-Dimensional Modeling of NOx and Soot Formation in DI-Diesel Engines Using Detailed Chemistry Based on the Interactive Flamelet Approach,” SAE Paper No. 962057.
Kong, S.-C. , Kim, H. , Reitz, R. D. , and Kim, Y. , 2006, “ Comparisons of Diesel PCCI Combustion Simulations Using a Representative Interactive Flamelet Model and Direct Integration of CFD With Detailed Chemistry,” ASME J. Eng. Gas Turbines Power, 129(1), pp. 252–260. [CrossRef]
D'Errico, G. , Lucchini, T. , Hardy, G. , Tap, F. , and Ramaekers, G. , 2015, “ Combustion Modeling in Heavy Duty Diesel Engines Using Detailed Chemistry and Turbulence-Chemistry Interaction,” SAE Paper No. 2015-01-0375.
Pope, S. B. , 2013, “ Small Scales, Many Species and the Manifold Challenges of Turbulent Combustion,” Proc. Combust. Inst. 34, pp. 1–31.
Van Oijen, J. , Lammers, F. , and De Goey, L. , 2001, “ Modeling of Complex Premixed Burner Systems by Using Flamelet-Generated Manifolds,” Combust. Flame, 127(3), pp. 2124–2134. [CrossRef]
Ihme, M. , Cha, C. M. , and Pitsch, H. , 2005, “ Prediction of Local Extinction and Re-Ignition Effects in Non-Premixed Turbulent Combustion Using a Flamelet/Progress Variable Approach,” Proc. Combust. Inst., 30(1), pp. 793–800. [CrossRef]
Fiorina, B. , Baron, R. , Gicquel, O. , Thevenin, D. , Carpentier, S. , and Darabiha, N. , 2003, “ Modelling Non-Adiabatic Partially Premixed Flames Using Flame-Prolongation of ILDM,” Combust. Theory Modell., 7(3), pp. 449–470. [CrossRef]
Ameen, M. M. , Kundu, P. , and Som, S. , 2016, “ Novel Tabulated Combustion Model Approach for Lifted Spray Flames With Large Eddy Simulations,” SAE Int. J. Engines, 9(4), pp. 2056–2065. [CrossRef]
Kundu, P. , Ameen, M. , Unnikrishnan, U. , and Som, S. , 2017, “ Implementation of a Tabulated Flamelet Model for Compression Ignition Engine Applications,” SAE Paper No. 2017-01-0564.
Kundu, P. , Echekki, T. , Pei, Y. , and Som, S. , 2017, “ An Equivalent Dissipation Rate Model for Capturing History Effects in Non-Premixed Flames,” Combust. Flame, 176, pp. 202–212. [CrossRef]
Kundu, P. , Ameen, M. M. , and Som, S. , 2017, “ Importance of Turbulence-Chemistry Interactions at Low Temperature Engine Conditions,” Combust. Flame, 183, pp. 283–298. [CrossRef]
Lu, L. , and Pope, S. B. , 2009, “ An Improved Algorithm for In Situ Adaptive Tabulation,” J. Comput. Phys., 228(2), pp. 361–386. [CrossRef]
Tap, F. , and Schapotschnikow, P. , 2012, “ Efficient Combustion Modeling Based on Tabkin® CFD Look-Up Tables: A Case Study of a Lifted Diesel Spray Flame,” SAE Paper No. 2012-01-0152.
Colin, O. , da Cruz, A. P. , and Jay, S. , 2005, “ Detailed Chemistry-Based Auto-Ignition Model Including Low Temperature Phenomena Applied to 3D Engine Calculations,” Proc. Combust. Inst., 30(2), pp. 2649–2656. [CrossRef]
Hindmarsh, A. C. , 1980, “ LSODE and LSODI, Two New Initial Value Ordinary Differential Equation Solvers,” ACM Signum Newsletter, 15(4), pp. 10–11.
Peters, N. , 1984, “ Laminar Diffusion Flamelet Models in Non-Premixed Turbulent Combustion,” Prog. Energy Combust. Sci., 10(3), pp. 319–339. [CrossRef]
Li, J. , Zhao, Z. , Kazakov, A. , and Dryer, F. L. , 2004, “ An Updated Comprehensive Kinetic Model of Hydrogen Combustion,” Int. J. Chem. Kinetics, 36(10), pp. 566–575. [CrossRef]
Luo, Z. , Yoo, C. S. , Richardson, E. S. , Chen, J. H. , Law, C. K. , and Lu, T. , 2012, “ Chemical Explosive Mode Analysis for a Turbulent Lifted Ethylene Jet Flame in Highly-Heated Coflow,” Combust. Flame, 159(1), pp. 265–274. [CrossRef]
Davis, S. G. , Joshi, A. V. , Wang, H. , and Egolfopoulos, F. , 2005, “ An Optimized Kinetic Model of H2/CO Combustion,” Proc. Combust. Inst., 30(1), pp. 1283–1292.
Lu, T. , and Law, C. K. , 2006, “ Linear Time Reduction of Large Kinetic Mechanisms With Directed Relation Graph: N-Heptane and Iso-Octane,” Combust. Flame, 144(1–2), pp. 24–36. [CrossRef]
Mehl, M. , Pitz, W. J. , Westbrook, C. K. , and Curran, H. J. , 2011, “ Kinetic Modeling of Gasoline Surrogate Components and Mixtures Under Engine Conditions,” Proc. Combust. Inst., 33(1), pp. 193–200. [CrossRef]
Mehl, M. , Curran, H. , Pitz, W. , and Westbrook, C. , 2009, “ Chemical Kinetic Modeling of Component Mixtures Relevant to Gasoline,” Fourth European Combustion Meeting, Vienna, Austria, Apr. 14–17.
Westbrook, C. K. , Pitz, W. J. , Herbinet, O. , Curran, H. J. , and Silke, E. J. , 2009, “ A Comprehensive Detailed Chemical Kinetic Reaction Mechanism for Combustion of n-Alkane Hydrocarbons From n-Octane to n-Hexadecane,” Combust. Flame, 156(1), pp. 181–199. [CrossRef]
CONVERGE, 2013, Software (Version 2.1. 0), Convergent Science, Middleton, WI.
Barths, H. , Hasse, C. , Bikas, G. , and Peters, N. , 2000, “ Simulation of Combustion in Direct Injection Diesel Engines Using a Eulerian Particle Flamelet Model,” Proc. Combust. Inst., 28(1), pp. 1161–1168. [CrossRef]
Xue, Q. , Som, S. , Senecal, P. K. , and Pomraning, E. , 2013, “ Large Eddy Simulation of Fuel-Spray Under Non-Reacting IC Engine Conditions,” Atomization Sprays, 23(10), pp. 925–955.
Kundu, P. , Pei, Y. , Wang, M. , Mandhapati, R. , and Som, S. , 2014, “ Evaluation of Turbulence-Chemistry Interaction Under Diesel Engine Conditions With Multi-Flamelet RIF Model,” Atomization Sprays, 24(9), pp. 779–800.
Luo, Z. , Som, S. , Sarathy, S. M. , Plomer, M. , Pitz, W. J. , Longman, D. E. , and Lu, T. , 2014, “ Development and Validation of an n-Dodecane Skeletal Mechanism for Spray Combustion Applications,” Combust. Theory Modell., 18(2), pp. 187–203. [CrossRef]
Ameen, M. M. , Pei, Y. , and Som, S. , 2016, “ Computing Statistical Averages From Large Eddy Simulation of Spray Flames,” SAE Paper No. 2016-01-0585.
Pei, Y. , Hu, B. , and Som, S. , 2016, “ Large-Eddy Simulation of an n-Dodecane Spray Flame Under Different Ambient Oxygen Conditions,” ASME J. Energy Resour. Technol., 138(3), p. 032205. [CrossRef]
Pei, Y. , Som, S. , Kundu, P. , and Goldin, G. M. , 2015, “ Large Eddy Simulation of a Reacting Spray Flame Under Diesel Engine Conditions,” SAE Paper No. 2015-01-1844.
Pei, Y. , Som, S. , Pomraning, E. , Senecal, P. K. , Skeen, S. A. , Manin, J. , and Pickett, L. M. , 2015, “ Large Eddy Simulation of a Reacting Spray Flame With Multiple Realizations Under Compression Ignition Engine Conditions,” Combust. Flame, 162(12), pp. 4442–4455. [CrossRef]
Van Dam, N. , Som, S. , Swantek, A. , and Powell, C. , 2016, “ The Effect of Grid Resolution on Predicted Spray Variability Using Multiple Large-Eddy Simulations,” ASME Paper No. ICEF2016-9384.
Skeen, S. A. , Manin, J. , and Pickett, L. M. , 2015, “ Simultaneous Formaldehyde PLIF and High-Speed Schlieren Imaging for Ignition Visualization in High-Pressure Spray Flames,” Proc. Combust. Inst., 35(3), pp. 3167–3174. [CrossRef]
Maes, N. , Meijer, M. , Dam, N. , Somers, B. , Toda, H. B. , Bruneaux, G. , Skeen, S. A. , Pickett, L. M. , and Manin, J. , 2016, “ Characterization of Spray a Flame Structure for Parametric Variations in ECN Constant-Volume Vessels Using Chemiluminescence and Laser-Induced Fluorescence,” Combust. Flame, 174, pp. 138–151. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Central processing unit time per time-step as a function of number of species in a mechanism for different chemistry solvers

Grahic Jump Location
Fig. 2

Total computational cost of generating flamelet libraries for different mechanisms as a function of the number of species

Grahic Jump Location
Fig. 3

Coupling of the TFM approach with the CFD code

Grahic Jump Location
Fig. 4

Transient flame development for the two mechanisms are shown using the OH mass fraction contours. The black iso-line represents the stoichiometric line.

Grahic Jump Location
Fig. 5

CH2O and OH formation versus time in the domain for both mechanisms

Grahic Jump Location
Fig. 6

Flame LOL evolution for the two mechanisms compared against experimental measurements

Grahic Jump Location
Fig. 7

Formation of CO (secondary Y axis) and C2H2 in the domain as a function of time for the different mechanisms

Grahic Jump Location
Fig. 8

Ignition delay versus ambient temperature conditions for different chemistry mechanisms and compared against the experimental data

Grahic Jump Location
Fig. 9

Flame liftoff versus ambient temperature conditions for different chemistry mechanisms and compared against the experimental data

Grahic Jump Location
Fig. 10

Flame structures at different ambient temperature conditions predicted by the two mechanisms. The OH mass fraction contours (left) and temperature contours (right) are shown.

Grahic Jump Location
Fig. 11

Single shot CH2O PLIF images from experiments are compared against model predictions from the detailed and reduced mechanisms for different times. The ambient conditions are for the baseline spray A case with an ambient temperature of 900 K and 15% ambient O2 concentration. The PLIF images are based on false color images.

Grahic Jump Location
Fig. 12

Ensemble-averaged OH PLIF data at the baseline spray A conditions (left) compared with the results from the detailed mechanism (center) and the reduced mechanism (right). The LES results are obtained by ensemble averaging over 3 realizations and azimuthal averaging over 128 planes.

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In