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Creation of small kinetic models for CFD applications: a meta-heuristic approach

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Abstract

This paper updates a method for generating small, accurate kinetic models for applications in computational fluid dynamics programs. This particular method first uses a time-integrated flux-based algorithm to generate the smallest possible skeletal model based on the detailed kinetic model. Then, it uses a multi-stage optimization process in which multiple runs of a genetic algorithm are used to optimize the rate constant parameters of the retained reactions. This optimization technique provides the user with the flexibility needed to balance the fidelity of the model with their time constraints. The updated method was applied to the reduction of a methane-air model under conditions meant to approximate the end of a compression stroke of an internal combustion engine. When compared to previous techniques, the results showed that this method could produce a more accurate model in considerably less time. The best model obtained in this study resulted in relative errors ranging from 0.22 to 1.14% on all six optimization targets. This reduced model was also able to adequately predict optimization targets for certain operating conditions, which were not included in the optimization process.

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Availability of data and materials

The reduced kinetic model developed in this study can be obtained in Online Resource 2. Raw data used to create the figures and tables shown in this manuscript can be obtained upon request.

Code availability

Authors wrote the main MATLAB script to call the built-in genetic algorithm and wrote the FORTRAN-77 codes needed in this study.

References

  1. Adbulrahman AM, Leylegian JC (2014) Incorporation of path flux and steepest descent methods in kinetic model reduction for CFD applications. In: 50th AIAA/ASME/SAE/ASEE joint propulsion conference and exhibition. AIAA Paper 2014–3663, Cleveland, OH, USA. https://doi.org/10.2514/6.2014-3663

  2. Edwards K, Edgar TF, Manousiouthakis VI (1998) Kinetic model reduction using genetic algorithms. Comput Chem Eng 22(1–2):239–246. https://doi.org/10.1016/S0098-1354(96)00362-6

    Article  Google Scholar 

  3. Elliott L, Ingham DB, Kyne AG, Mera NS, Pourkashanian M, Wilson CW (2002) A real coded genetic algorithm for the optimization of reaction rate parameters for chemical kinetic modeling in a perfectly stirred reactor. In: Proceedings of the 4th annual conference on genetic and evolutionary computation. New York, New York, USA, p 1261

  4. Elliott L, Ingham DB, Kyne AG, Mera NS, Pourkashanian M, Wilson CW (2003) Incorporation of physical bounds on rate parameters for reaction mechanism optimization using genetic algorithms. Combust Sci Technol 175(4):619–648. https://doi.org/10.1080/00102200302389

    Article  MATH  Google Scholar 

  5. Elliott L, Ingham DB, Kyne AG, Mera NS, Pourkashanian M, Wilson CW (2004) Genetic algorithms for optimization of chemical kinetics reaction mechanisms. Prog Energy Combust Sci 30:297–328. https://doi.org/10.1016/j.pecs.2004.02.002

    Article  MATH  Google Scholar 

  6. Elliott L, Ingham DB, Kyne AG, Mera NS, Pourkashanian M, Wilson CW (2005) The use of ignition delay time in genetic algorithms optimization of chemical kinetics reaction mechanisms. Eng Appl Artif Intell 18(7):825–831. https://doi.org/10.1016/j.engappai.2005.02.006

    Article  Google Scholar 

  7. Elliott L, Ingham DB, Kyne AG, Mera NS, Pourkashanian M, Whittaker S (2006) Reaction mechanism reduction and optimization for modelling aviation fuel oxidation using standard and hybrid genetic algorithms. Comput Chem Eng 30(5):889–900. https://doi.org/10.1016/j.compchemeng.2006.01.003

    Article  Google Scholar 

  8. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:273–280. https://doi.org/10.1016/j.eswa.2020.113377

    Article  Google Scholar 

  9. Frenklach M, Wang H, Rabinowitz MJ (1992) Optimization and analysis of large chemical kinetics mechanisms using solution mapping method—combustion of methane. Prog Energy Combust Sci 18(1):47–73. https://doi.org/10.1016/0360-1285(92)90032-V

    Article  Google Scholar 

  10. Galvin D, Cremasco H, Gomes Mantovani AC, Bona E, Killner M, Borsato D (2020) Kinetic study of the transesterification reaction by artificial neural networks and parametric particle swarm optimization. Fuel 267:117221. https://doi.org/10.1016/j.fuel.2020.117221

    Article  Google Scholar 

  11. Harris SD, Elliott L, Ingham DB, Pourkashanian M, Wilson CW (2000) The optimization of reaction rate parameters for chemical kinetic modelling of combustion using genetic algorithms. Comput Methods Appl Mech Eng 190(8–10):1065–1090. https://doi.org/10.1016/S0045-7825(99)00466-1

    Article  MATH  Google Scholar 

  12. Kee RJ, Grcar JF, Smooke MD, Miller JA (1985) A fortran program for modeling steady laminar one-dimensional premixed flames. Sandia National Labs., Albuquerque, NM, Rept. SAND85-8240

  13. Kee RJ, Rupley FM, Miller JA (1985b) CHEMKIN-II: a fortran chemical kinetics package for the analysis of gas-phase chemical kinetics. Sandia National Labs., Albuquerque, NM, Rept. SAND89-8009

  14. Kee RJ, Dixon-Lewis G, Warnatz J, Coltrin ME, Miller JA (1986) A fortran computer code package for the evaluation of gas-phase multicomponent transport properties. Sandia National Labs., Albuquerque, NM, Rept. SAND86-8246

  15. Law CK, Sung CJ, Wang H, Lu TF (2003) Development of comprehensive detailed and reduced reaction mechanisms for combustion modeling. AIAA J 41(9):1629–1646. https://doi.org/10.2514/2.7289

    Article  Google Scholar 

  16. Leylegian JC (2018) Creation of small skeletal models in kinetic model reductions. AIAA J Propuls Power 34(5):239–246. https://doi.org/10.2514/1.B36913

    Article  Google Scholar 

  17. Leylegian JC, Paul TV, Tulino VA (2013) Method of kinetic model reduction for computational fluid dynamics applications. AIAA J Propuls Power 29(5):1231–1243. https://doi.org/10.2514/1.B34805

    Article  Google Scholar 

  18. Lu T, Law CK (2005) A directed relation graph method for mechanism reduction. Proc Combust Inst 30(1):1333–1341. https://doi.org/10.1016/j.proci.2004.08.145

    Article  Google Scholar 

  19. Lu T, Ju Y, Law CK (2001) Complex CSP for chemistry reduction and analysis. Combust Flame 126(1–2):1445–1455. https://doi.org/10.1016/S0010-2180(01)00252-8

    Article  Google Scholar 

  20. Lu TF, Law CK (2006) Linear time reduction of large kinetic mechanisms with directed relation graph: n-heptane and iso-octane. Combust Flame 144(1–2):24–36. https://doi.org/10.1016/j.combustflame.2005.02.015

    Article  Google Scholar 

  21. Lutz AE, Kee RJ, Miller JA (1987) SENKIN: a fortran program for predicting homogeneous gas phase chemical kinetics with sensitivity analysis. Sandia National Labs., Albuquerque, NM, Rept. SAND87-8248

  22. Maas U, Pope SB (1992) Simplifying chemical kinetics: intrinsic low-dimensional manifolds in composition space. Combust Flame 88(3–4):239–264. https://doi.org/10.1016/0010-2180(92)90034-M

    Article  Google Scholar 

  23. Michalewicz Z (1996) Genetic algorithms: why do they work? genetic algorithms and data structures = evolution programs, 3rd edn. Springer, Berlin, pp 44–57

    Book  Google Scholar 

  24. Mishra M, Peiperl L, Reuven Y, Rabitz H, Yetter RA, Smooke MD (1991) Use of Green’s function for analysis of dynamic couplings: some example from chemical kinetics and quantum dynamics. J Phys Chem 95(8):3109–3118. https://doi.org/10.1021/j100161a029

    Article  Google Scholar 

  25. Montgomery CJ, Yang C, Parkinson AR, Chen JY (2006) Selecting the optimum quasi-steady-state species for reduced chemical kinetic mechanisms using a genetic algorithm. Combust Flame 144(1–2):37–52. https://doi.org/10.1016/j.combustflame.2005.06.011

    Article  Google Scholar 

  26. Neshat E, Saray RK (2015) An optimized chemical kinetic mechanism for HCCI combustion of PRFs using multi-zone model and genetic algorithm. Energy Convers Manag 92(1):172–183. https://doi.org/10.1016/j.enconman.2014.11.057

    Article  Google Scholar 

  27. Pepiot-Desjardins P, Pitsch H (2008) An efficient error-propagation-based reduction method for large chemical kinetic mechanisms. Combust Flame 154(1–2):67–81. https://doi.org/10.1016/j.combustflame.2007.10.020

    Article  MATH  Google Scholar 

  28. Rahimi A, Fatehifar E, Saray RK (2010) Development of an optimized chemical kinetic mechanism for homogeneous charge compression ignition combustion of a fuel blend of n-heptane and natural gas using a genetic algorithm. In: Proceedings of the institution of mechanical engineers, Part D: Journal of Automobile Engineering, vol 224. SAGE, pp 1141–1159

  29. Rogers RC, Chinitz W (2012) Using a global hydrogen-air combustion model in turbulent reacting flow calculations. AIAA J 24(4):586–592. https://doi.org/10.2514/3.8117

    Article  Google Scholar 

  30. Siouris S, Blakey S (2019) Fitness functions for evolutionary optimization of rate parameters in chemically reacting systems. Chem Eng Sci 196(16):354–365. https://doi.org/10.1016/j.ces.2018.11.009

    Article  Google Scholar 

  31. Smith GP, Golden DM, Frenklach M, Moriarty NW, Eiteneer B, Goldenberg M, Bowman CT, Hanson RK, Song S, Gardiner, Jr W, Lissianski VV, Qin Z (n.d.) GRI-Mech 3.0. http://combustion.berkeley.edu/gri-mech/version30/text30.html. Accessed 1 Sept 2011

  32. Sun W, Chen Z, Gou X, Ju Y (2010) A path flux analysis method for the reduction of detailed chemical kinetic mechanisms. Combust Flame 157(7):1298–1307. https://doi.org/10.1016/j.combustflame.2010.03.006

    Article  Google Scholar 

  33. The MathWorks Inc (2019) MATLAB optimization toolbox R2019b. Natick, MA, USA. https://www.mathworks.com/products/optimization.html. Accessed 20 May 2020

  34. The MathWorks Inc (2020) Genetic algorithm options (R2020a). https://www.mathworks.com/help/gads/genetic-algorithm-options.html. Accessed 20 May 2020

  35. The MathWorks Inc (2020) How the genetic algorithm works (R2020a). https://www.mathworks.com/help/gads/how-the-genetic-algorithm-works.html. Accessed 20 May 2020

  36. Till Z, Chovan T, Varga T (2020) Improved understanding of reaction kinetic identification problems using different nonlinear optimization algorithms. J Taiwan Inst Chem Eng 111:73–79. https://doi.org/10.1016/j.jtice.2020.05.013

    Article  Google Scholar 

  37. Turns SR (2012) Laminar premixed flames. An introduction to combustion concepts and applications, 3rd edn. The McGraw-Hill Companies, New York, pp 258–331

    Google Scholar 

  38. Wan K, Vervisch L, Gao Z, Domingo P, Jiang C, Xia J, Wang Z (2020) Development of reduced and optimized reaction mechanism for potassium emissions during biomass combustion based on genetic algorithms. Energy 211:118565. https://doi.org/10.1016/j.energy.2020.118565

    Article  Google Scholar 

  39. Wang H, Frenklach M (1991) Detailed reduction of reaction mechanisms for flame modeling. Combust Flame 87(3–4):365–370. https://doi.org/10.1016/0010-2180(91)90120-Z

    Article  Google Scholar 

  40. Wang H, You X, Joshi AV, Davis SG, Laskin A, Egolfopoulos F, Law CK (2007) USC Mech Version II. High-temperature combustion reaction model of H\(_2\)/CO/C\(_1\)-C\(_4\) compounds. Univ. of Southern California Combustion Kinetics Lab. http://ignis.usc.edu/USC_Mech_II.htm. Accessed 14 March 2013

  41. Wang H, Dames E, Sirjean B, Sheen DA, Tango R, Violo A, Lai JYW, Egolfopoulos FN, Davidson DF, Hanson RK, et al (2010) JetSurf 2.0: a high-temperature chemical kinetic model of n-alkane (up to n-dodecane), cyclohexane, and methyl-, ethyl-, n-propyl and n-butyl-cyclohexane oxidation at high temperatures. http://web.stanford.edu/group/haiwanglab/JetSurF/JetSurF2.0/index.html. Accessed 22 May 2017

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Manhattan College, Riverdale, NY, USA

    Michael A. Calicchia, Ehsan Atefi & John C. Leylegian

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  1. Michael A. Calicchia
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  2. Ehsan Atefi
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  3. John C. Leylegian
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Michael A. Calicchia, who also wrote the first draft of the manuscript. All authors commented on previous versions of the manuscript and approved the final manuscript.

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Correspondence to Ehsan Atefi.

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Calicchia, M.A., Atefi, E. & Leylegian, J.C. Creation of small kinetic models for CFD applications: a meta-heuristic approach. Engineering with Computers 38 (Suppl 3), 1923–1937 (2022). https://doi.org/10.1007/s00366-021-01352-4

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