کاهش مکانیزم احتراقی سوخت دی‌متیل‌اتر با استفاده از الگوریتم‌های بهینه‌سازی ازدحام ذرات، تکامل تفاضلی و مدولاسیون زاویه‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک، دانشگاه بیرجند

2 گروه مکانیک- دانشکده مهندسی- دانشگاه بیرجند- بیرجند- ایران

چکیده

مدل ­سازی ریاضی به ‌عنوان ابزاری برای پیش­بینی رفتار احتراقی سوخت­ ها به‌کار می­ رود. استفاده از ساز و کارهای سینتیک شیمیایی مفصل در مدل­های احتراقی به افزایش قابل توجه زمان محاسبات می­ انجامد. الگوریتم­ های بهینه­ سازی فراابتکاری یکی از روش­ های مورد استفاده برای کاهش مکانیزم­ های مفصل است. هدف این مقاله، بررسی امکان استفاده از الگوریتم­ های فراابتکاری پیوسته در فضای دودویی برای کاهش مکانیزم احتراقی سوخت دی­ متیل ­اتر است. به این منظور از الگوریتم­ های ازدحام ذرات و تکامل تفاضلی استفاده شده و با استفاده از نگاشتی تحت عنوان مدولاسیون زاویه ­ای برای نگاشت فضای پیوسته به فضای دودویی، تعداد متغیرهای مسئله از 351 به 6 متغیر کاهش یافته است. در نهایت، از مکانیزم مفصل دی­ متیل ­اتر شامل 351 واکنش ابتدایی و 79 گونه شیمیایی، مکانیزم کاهش یافته ­ای با 43 واکنش ابتدایی و 17 گونه به دست آمده که نتایج حاصل از به­ کارگیری آن در شبیه­ سازی دو راکتور فشار ثابت و شعله آرام پیش­ آمیخته، حداکثر خطای 95/0 درصد را نسبت به مکانیزم مفصل نشان می ­دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Automatic reduction of detailed combustion mechanisms using particle swarm optimization, differential evolution and angular modulation algorithms: application to Dimethyl Ether/air combustion

نویسندگان [English]

  • Mohsen Mousavi 1
  • Javad Khadem 2
  • Ali Safavinezhad 1
1 Department of Mechanical Enginnering, University of Birjand
2 Mechanical Eng, University of Birjand, Birjand, Iran
چکیده [English]

Mathematical modeling is used as a tool to predict the combustion behavior of fuels. The use of detailed chemical kinetics mechanisms in combustion models increases computational time. Metaheuristic optimization algorithms are one of the methods used to reduce the detailed mechanisms. The purpose of this paper is to investigate the possibility of using continuous metaheuristic algorithms in binary space to reduce the combustion mechanism of dimethyl ether fuel. For this purpose, particle swarm optimization and differential evolution algorithms have been used in a combination with angular modulation to map the continuous space to binary one and reduce the dimensions of problem from 351 to 6. Finally, a detailed mechanism with 79 species and 351 reactions is reduced to 17 species and 43 reactions. It has been showed that the reduced mechanism predicts the results of detailed mechanism in constant pressure and laminar premixed flame reactors very well with maximum error less than % 0.95.
 
.

کلیدواژه‌ها [English]

  • Chemical kinetics
  • Reduced mechanism
  • Metaheuristic optimization algorithms
  • Angular modulation

مراجع

  1. Tong, B. Zhong, Y. Pei and T. Lu, A Hybrid Mechanism for n-Dodecane Combustion with Optimized Low-Temperature Chemistry, 9th U.S. National Combustion Meeting, Cincinnati, Ohio, 2015.
  2. Lu, S. Som, S.M. Sarathy, M. Plomer, W.J. Pitz, D.E. Longman and T.F. Lu, “Development and validation of an n-dodecane skeletal mechanism for Diesel spray-combustion applications”, Combustion Theory and Modelling,Vol. 18, 2014, pp. 187–203.
  3. Saltelli, M. Ratto, S. Tarantola and F. Campolongo, “Sensitivity analysis for chemical models”, Chemical Reviews, Vol. 105, 2005, pp. 2811-2828.
  4. Y. Luo, T.F. Lu, M.J. Maciaszek, S. Som, and D.E. Longman, “A reduced mechanism for high temperature oxidation of biodiesel surrogates”, Energy and Fules, Vol. 24, 2010, pp. 6283-6293.
  5. Luo, S. Som, S.M. Sarathy, M. Plomer, W.J. Pitz, D.E. Longman and T.F. Lu, “Development and validation of an n-dodecane skeletal mechanism for Diesel spray-combustion applications”, Combustion Theory and Modelling, Vol. 18, 2014, pp. 187–200.
  6. Tosatto, B.A.V. Bennett and M.D. Smooke, “Comparison of different DRG-based methods for the skeletal reduction of JP-8 surrogate mechanisms”. Combustion and Flame, Vol. 160, 2013, pp. 1572–1582.
  7. Zandie, H. Kiat Ng, S. Gan, M.F.M said, X. Cheng, “Review of the advances in integrated chemical kinetics-computational fluid dynamics combustion modelling studies of gasoline-biodiesel mixtures”, Transportation Engineering, Vol. 7, 2022, pp. 34-52.
  8. Zandie, H. Kiat Ng, S. Gan, M.F.M said, X. Cheng, “Development of a reduced multi-component chemical kinetic mechanism for the combustion modelling of diesel-biodiesel-gasoline mixtures”, Transportation Engineering, Vol. 7, 2022, pp. 13-33.
  9. Turanyi and A.S. Tomlin, Analysis of Kinetic Reaction Mechanisms, Springer, 2014.
  10. Warnatz, U. Maas and R.W. Dibble, Combustion: Physical and Chemical Fundamentals,Modeling and Simulation, Experiments, Pollutant Formation, Springer, 2006.
  11. H. Lam, Singular perturbation for stiff equations using numerical methods, Recent Advances in the Aerospace Sciences: In Honor of Luigi Crocco on His Seventy-fifth Birthday, 1985, pp. 3-19.
  12. H. Lam, “Using CSP to understand complex chemical kinetics”, Combustion Science and Technology,Vol. 89, 1993, pp. 375-404.
  13. Maas and S. Pope, “Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space”, Combustion and Flame, Vol. 88, 1992, pp. 239-264.
  14. N. Ghorban, “Model reduction in chemical dynamics: slow invatiant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph”, Cuurent Opinion in Chemical Engineering, Vol. 21, 2018, pp. 48-59.
  15. Saggese, A. Frassoldati, A. “Cuoci, T. Faravelli and E. Ranzi, A lumped approach to the kinetic modeling of pyrolysis and combustion of biodiesel fuels”. Proceedings of the Combustion Institute, Vol. 34, 2013, pp. 427-434.
  16. Zhou and H. Wei, “An investigation of in situ adaptive tabulation for premixed and nonpremixed conbustion engine simulations with primary reference fuel mechanism”, Applied Thermal Engineering, Vol. 111, 2017, pp. 526-536.
  17. Kourdis and J. Bellan, “Highly Reduced Species Mechanisms for iso-Cetane Using the Local Self-Similarity Tabulation Method”, Chemical Kinetics, Vol. 11, 2016, pp. 739-752.
  18. Edwards, T.F. Edgar and V.I. Manousiouthakis, “Kinetic model reduction using genetic algorithms”, Computers and Chemical Engineering, Vol. 22, 1998, pp. 239–246.
  19. Elliott, D.B. Ingham, A.G. Kyne, N.S. Mera, M. Pourkashanian and C.W. Wilson, “Genetic algorithms for optimisation of chemical kinetics reaction mechanisms”, Progress in Energy and Combustion Science, Vol. 30, 2004, pp. 297–328.
  20. Elliott, D.B. Ingham, A.G. Kyne, N.S. Mera, M. Pourkashanian and C.W. Wilson, “Reaction mechanism reduction and optimization using genetic algorithms”, Industrial and Engineering Chemistry Research, Vol. 44, 2005, pp. 658–667.
  21. Elliott, D.B. Ingham, A.G. Kyne, N.S. Mera, M. Pourkashanian and S. Whittaker, “Reaction mechanism reduction and optimisation for modelling aviation fuel oxidation using standard and hybrid genetic algorithms”, Computers and Chemical Engineering, Vol. 30, 2006, pp. 889–900.
  22. R. Maurya, S. Katare, P.R. Patkar, A.E. Rundell and V. Venkatasubramanian, “A systematic framework for the design of reduced-order models for signal transduction pathways from a control theoretic perspective”, Computers and Chemical Engineering, Vol. 30, 2006, pp. 437–452.
  23. R. Maurya, S.J. Bornheimer, V. Venkatasubramanian and S. Subramaniam, “Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks”, IET Systems Biology, Vol. 3, 2009, pp. 24–39.
  24. J. Montgomery, C. Yang, A.R. Parkinson and J.Y. Chen, “Selecting the optimum quasi-steadystate species for reduced chemical kinetic mechanisms using a genetic algorithm”, Combustion and Flame, Vol. 144, 2006, pp. 37–52.
  25. J. Hernandez, R. Ballesteros and J. Sanzargent, “Reduction of kinetic mechanisms for fuel oxidation through genetic algorithms”, Mathematical and Computer Modelling, Vol. 52, 2010, pp. 1185–1193.
  26. Sikalo, O. Hasemann, C. Schulz, A. Kempf and I. Wlokas, “A genetic algorithm-based method for the automatic reduction of reaction mechanisms”, Chemical Kinetics, Vol. 46, 2014, pp. 41–59.
  27. llnl.gov
  28. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces”, Global Optimization, Vol. 11, 1997, pp. 359-431.
  29. P. Engelbrecht, Computational Intelligence, John Wiley & Sons Ltd, 2007.
  30. Kennedy and R.C. Eberhart, Particle Swarm Optimization, International Conference on Neural Networks, Perth, Australia, 1995.
  31. Pampara, A.P. Engelbrecht and N. Franken, Binary Differential Evolution, IEEE International Conference on Evolutionary Computation, Vancouver, Canada, 2006.
  32. Dong, Z. Wang and J. Mo, “A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization”, Applied Sciences, 2021, Vol. 11, pp. 2243-2262.
  33. Wang, R. Shi and J. Dong, “A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems”, Entropy, Vol. 23, 2021, pp. 598-622
  34. L. Barend and A.P. Engelbrecht, Angle Modulated Particle Swarm Variants, 9th International Conference on Swarm Intelligence, Brussels, Belgium, 2014.

 

سوخت و احتراق
دوره 15، شماره 1 - شماره پیاپی 38
اردیبهشت 1401
صفحه 102-122

فایل ها

سابقه مقاله

  • تاریخ دریافت: 10 اردیبهشت 1401
  • تاریخ بازنگری: 29 خرداد 1401
  • تاریخ پذیرش: 08 تیر 1401

هم رسانی

ارجاع به این مقاله

آمار