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Optimization method for the component of aviation kerosene surrogate fuels based on chemical reactor network model

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Abstract

On the basis of the simulation results of premixed jet flame using liquid hydrocarbon fuel, an optimization method for the component of aviation kerosene surrogate fuels based on chemical reactor network (CRN) model is proposed. Firstly, computational fluid dynamics (CFD) numerical simulation is carried out for three hydrocarbon fuel jet flames of n-dodecane (C12H26), n-decane (C10H22), n-heptane (C7H16) to obtain the characteristics of the temperature field under different working conditions. Based on this, the CRN partition topological geometry of jet flame is generated. The genetic algorithm is used to optimize the parameters of each reactor, and the algebraic relationship between the CRN parameter and the inlet parameters (dimensionless inlet temperature, Reynolds number and mixing fraction) is obtained. Then, topological geometry of the CRN which is suitable for different working conditions is established, and the best CRN model of hydrocarbon fuel jet flame is constructed. Combined with the experimental data, the optimal proportion of three-component surrogate fuels is determined by the above method. As a result, the optimal ratio is 56.41% C12H26, 26.36% C10H22 and 17.23% C7H16. Finally, the CFD numerical simulation was performed and the results agreed well with the experimental data, indicating the effectiveness of optimization method for the CRN-based aviation kerosene surrogate fuels. This method can be applied to the component optimization of aviation kerosene surrogate fuels.

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References

  1. Wen Z, Jingchen L, Xiaoxiao C et al (2011) Detailed reaction kinetic modeling of n-decane premixed combustion. J Aeronaut Power 26:2258–2266

    Google Scholar 

  2. Fang Z (2018) Study on the mechanism reduction for kerosene combustion and the ignition and extinction combustion characteristics. Southwest University of Science and Technology, Mianyang

    Google Scholar 

  3. Bikas G, Peters N (2001) Kinetic modelling of n-decane combustion and autognition: modelling combustion of n-decane. Combust Flame 126:1456–1475

    Article  Google Scholar 

  4. Wang TS (2001) Thermophysics characterization of kerosene combustion. J Thermophys Heat Transf 15:140–147

    Article  Google Scholar 

  5. Patterson PM, Kyne AG, Pourkashanian M et al (2000) Combustion of kerosene in counter flow diffusion flames. J Propul Power 16:453–460

    Google Scholar 

  6. Honnet S, Seshadri K, Niemann U et al (2009) A surrogate fuel for kerosene. Proc Combust Inst 32:485–492

    Article  Google Scholar 

  7. Vovelle C, Delfau JL, Reuillon M (1994) Formation of aromatic hydrocarbons in decane and kerosene flames at reduced pressure. Soot formation in combustion. Springer, Berlin

    Google Scholar 

  8. Dagaut P, Ristori A, Bakali AE et al (2002) Experimental and kinetic modeling study of the oxidation of n-propylbenzene. Fuel 81:173–184

    Article  Google Scholar 

  9. Montgomery CJ, Cannon SM, Mawid MA et al (2002) Reduced chemical kinetic mechanisms for JP-8combustion. AIAA:0336

  10. Violi A, Yan S, Eddings EG et al (2002) Experimental formulation and kinetic model for JP-8surrogate mixtures. Combust Sci Technol 174:399–417

    Article  Google Scholar 

  11. Wen F, Yingwen Y, Yunpeng L et al (2018) Simplified Mechanism verification of three component surrogate fuels for RP-3 aviation kerosene. J Aeronaut Power 33:2101–2111

    Google Scholar 

  12. Yan Y, Liu Y, Fang W et al (2018) A simplified chemical reaction mechanism for two-component RP-3kerosene surrogate fuel and its verification. Fuel 227:127–134

    Article  Google Scholar 

  13. Weiming Y (2014) Study on flame speed and chemical reaction mechanism for alternative fuels of aviation kerosene. Tsinghua University, Beijing

    Google Scholar 

  14. Bragg SL (1953) Rolls royce internal report

  15. Merino Madrid C (2017) Chemical reactor network for LDI combustor. Delft University of Technology

    Google Scholar 

  16. Mavris D (2010) Enhanced emission prediction modeling and analysis for conceptual design. Georgia Institute of Technology

    Google Scholar 

  17. Lyra S, Cant RS (2013) Analysis of high pressure premixed flames using equivalent reactor networks for predicting NOx emissions. Fuel 107:261–268

    Article  Google Scholar 

  18. Innocenti A, Andreini A, Bertini D (2018) Turbulent flow-field effects in a hybrid CFD-CRN model for the prediction of NOx, and CO emissions in aero-engine combustors. Fuel 215:853–864

    Article  Google Scholar 

  19. Depape P, Novosselov I (2018) Model-based approach for combustion monitoring using real-time chemical reactor network. J Combust 2018:1–12

    Article  Google Scholar 

  20. Monaghan RFD, Tahir R, Bourque G et al (2014) Detailed emissions prediction for a turbulent swirling nonpremixed flame. Energy Fuels 28:1470–1488

    Article  Google Scholar 

  21. Changsong H (2008) Network based simulation and analysis of combustion process in gas turbines. Tsinghua University, Beijing

    Google Scholar 

  22. Guochang W, Jianpeng Z, Jianchun M (2016) Establishment and application of PSR network model for MILD combustion. J Eng Thermophys 37:201–208

    Google Scholar 

  23. Zhao L (2019) Study on prediction of performance of chemical reactor network for combustor with high precision. Nanjing University of Aeronautics and Astronautics, Nanjing

    Google Scholar 

  24. Bin M (2019) Numerical investigation of NOx emission of lean premixed combustor using chemical reactor network model. University of Chinese Academy of Sciences, Beiing

    Google Scholar 

  25. ANSYS Fluent Theory Guide version 15.0.Ansys Inc. USA

  26. Fichet V, Kanniche M, Plion P et al (2010) A reactor network model for predicting NOx emissions in gas turbines. Fuel 89:2202–2210

    Article  Google Scholar 

  27. Kee RJ, Rupley FM, Meeks E et al (1991) CHEMKIN-III: A fortran chemical kinetics package for the analysis of gas phase chemical and plasma kinetics. Sandia Natl Lab Rep 96:142–146

    Google Scholar 

  28. Merino Madrid C (2017) Chemical reactor network for LDI combustor: CRN development and analysis of different fuels. Delft University of Technology

    Google Scholar 

  29. Jie B (2017) Fault diagnosis of bearing combining parameter optimized variational mode decomposition based on genetic algorithm with 1.5-dimensional spectrum. J Propuls Technol 38:1618–1624

    Google Scholar 

  30. Chongde Lu (2015) Thermal parameter measurement and processing. Tsinghua University Press, Beijing

    Google Scholar 

Download references

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Correspondence to Danwei Zheng.

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Technical Editor: Mario Eduardo Santos Martins.

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Zheng, D., Liu, Y., Zhang, X. et al. Optimization method for the component of aviation kerosene surrogate fuels based on chemical reactor network model. J Braz. Soc. Mech. Sci. Eng. 43, 243 (2021). https://doi.org/10.1007/s40430-021-02958-x

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  • DOI: https://doi.org/10.1007/s40430-021-02958-x

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