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
Wen Z, Jingchen L, Xiaoxiao C et al (2011) Detailed reaction kinetic modeling of n-decane premixed combustion. J Aeronaut Power 26:2258–2266
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
Bikas G, Peters N (2001) Kinetic modelling of n-decane combustion and autognition: modelling combustion of n-decane. Combust Flame 126:1456–1475
Wang TS (2001) Thermophysics characterization of kerosene combustion. J Thermophys Heat Transf 15:140–147
Patterson PM, Kyne AG, Pourkashanian M et al (2000) Combustion of kerosene in counter flow diffusion flames. J Propul Power 16:453–460
Honnet S, Seshadri K, Niemann U et al (2009) A surrogate fuel for kerosene. Proc Combust Inst 32:485–492
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
Dagaut P, Ristori A, Bakali AE et al (2002) Experimental and kinetic modeling study of the oxidation of n-propylbenzene. Fuel 81:173–184
Montgomery CJ, Cannon SM, Mawid MA et al (2002) Reduced chemical kinetic mechanisms for JP-8combustion. AIAA:0336
Violi A, Yan S, Eddings EG et al (2002) Experimental formulation and kinetic model for JP-8surrogate mixtures. Combust Sci Technol 174:399–417
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
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
Weiming Y (2014) Study on flame speed and chemical reaction mechanism for alternative fuels of aviation kerosene. Tsinghua University, Beijing
Bragg SL (1953) Rolls royce internal report
Merino Madrid C (2017) Chemical reactor network for LDI combustor. Delft University of Technology
Mavris D (2010) Enhanced emission prediction modeling and analysis for conceptual design. Georgia Institute of Technology
Lyra S, Cant RS (2013) Analysis of high pressure premixed flames using equivalent reactor networks for predicting NOx emissions. Fuel 107:261–268
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
Depape P, Novosselov I (2018) Model-based approach for combustion monitoring using real-time chemical reactor network. J Combust 2018:1–12
Monaghan RFD, Tahir R, Bourque G et al (2014) Detailed emissions prediction for a turbulent swirling nonpremixed flame. Energy Fuels 28:1470–1488
Changsong H (2008) Network based simulation and analysis of combustion process in gas turbines. Tsinghua University, Beijing
Guochang W, Jianpeng Z, Jianchun M (2016) Establishment and application of PSR network model for MILD combustion. J Eng Thermophys 37:201–208
Zhao L (2019) Study on prediction of performance of chemical reactor network for combustor with high precision. Nanjing University of Aeronautics and Astronautics, Nanjing
Bin M (2019) Numerical investigation of NOx emission of lean premixed combustor using chemical reactor network model. University of Chinese Academy of Sciences, Beiing
ANSYS Fluent Theory Guide version 15.0.Ansys Inc. USA
Fichet V, Kanniche M, Plion P et al (2010) A reactor network model for predicting NOx emissions in gas turbines. Fuel 89:2202–2210
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
Merino Madrid C (2017) Chemical reactor network for LDI combustor: CRN development and analysis of different fuels. Delft University of Technology
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
Chongde Lu (2015) Thermal parameter measurement and processing. Tsinghua University Press, Beijing
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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