基于显式函数和神经网络的喷气燃料混合模型的研究及应用
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摘要
针对喷气燃料复杂的理化性质,采取显式函数和人工神经网络相结合的策略,以氢碳比、黏度和蒸馏曲线等作为目标性质,发展了喷气燃料代用组分的构建方法,对混合燃料的理化性质具有很好的预测精度.并将此混合模型应用于煤基喷气燃料的代用组分构建,所获得的代用组分模型与真实燃料之间的理化性质差异符合模拟要求.
Surrogate model of jet fuel is one of the most important premises to high-fidelity numerical simulations of spray combustion in aero-engine.For complex physico-chemical properties of jet fuel,a strategy was proposed based on a mixing model combining explicit equations and implicit neural networks.Hydrogen-carbon ratio,viscosity,and distillation curve were chosen as the target properties,and the method of jet fuel surrogate formulation was developed,which could well simulate the target properties of mixture.The mixing model was used to build the surrogate of coal-derived synthetic jet fuel,and the difference of physico-chemical properties between surrogate components and real fuel could meet the simulating demands.
Surrogate model of jet fuel is one of the most important premises to high-fidelity numerical simulations of spray combustion in aero-engine.For complex physico-chemical properties of jet fuel,a strategy was proposed based on a mixing model combining explicit equations and implicit neural networks.Hydrogen-carbon ratio,viscosity,and distillation curve were chosen as the target properties,and the method of jet fuel surrogate formulation was developed,which could well simulate the target properties of mixture.The mixing model was used to build the surrogate of coal-derived synthetic jet fuel,and the difference of physico-chemical properties between surrogate components and real fuel could meet the simulating demands.
引文
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[4]侯再坚,胡泽祥.燃料烃类组成与理化性能的关系方程综述[J].石油商技,2015,33(3):20-23.Hou Zaijian,Hu Zexiang.Summary of fuel hydrocarbon composition and physicochemical properties of relation equations[J].Petroleum Products Application Research,2015,33(3):20-23(in Chinese).
[5]Grunberg L,Nissan A H.Mixture law for viscosity[J].Nature,1949,164(4175):799-800.
[6]Rocabruno-Valdés C I,Ramírez-Verduzco L F,Hernández J A.Artificial neural network models to predict density,dynamic viscosity,and cetane number of biodiesel[J].Fuel,2015,147:9-17.
[7]Filho A O B,Barros A K D,Labidi S,et al.Application of artificial neural networks to predict viscosity,iodine value and induction period of biodiesel focused on the study of oxidative stability[J].Fuel,2015,145(145):127-135.
[8]Zhang C,Hui X,Lin Y,et al.Recent development in studies of alternative jet fuel combustion:Progress,challenges,and opportunities[J].Renewable and Sustainable Energy Reviews,2016,54:120-138.
[9]黄勇,林宇震,樊未军,等.燃烧与燃烧室[M].北京:北京航空航天大学出版社,2009.Huang Yong,Lin Yuzhen,Fan Weijun,et al.Combustion and Combustor[M].Beijing:Beihang University Press,2009(in Chinese).
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[12]夏云龙,项曙光.电解质NRTL模型的发展及应用[J].河北化工,2004,27(1):9-12.Xia Yunlong,Xiang Shuguang.Development and application of electrolyte NRTL model[J].Hebei Chemical Engineering,2004,27(1):9-12(in Chinese).
[13]王俊清.BP神经网络及其改进[J].重庆工学院学报,2007,21(3):75-77.Wang Junqing.BP neural network and its improvement[J].Journal of Chongqing Institute of Technology,2007,21(3):75-77(in Chinese).
[14]黄丽.BP神经网络算法改进及应用研究[D].重庆:重庆师范大学计算机与信息科学学院,2008.Huang Li.BP Neural Network Algorithm Improvement and Application Research[D].Chongqing:College of Computer and Information Science,Chongqing Normal University,2008(in Chinese).
[15]柳松青.MATLAB神经网络BP网络研究与应用[J].计算机工程与设计,2003,24(11):81-83,88.Liu Songqing.Research and application on MATLAB BP neural nerwork[J].Computer Engineering and Design,2003,24(11):81-83,88(in Chinese).
[16]Garson G D.Interpreting neural-network connection weights[J].AI Expert,1991,6(4):46-51.
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[19]王徳岩.高热安定性喷气燃料JP900制备及性能[J].能源研究与信息,2006,22(4):232-236.Wang Deyan.Preparation and performance of the jet fuel JP900 of high thermal stability[J].Energy Research and Information,2006,22(4):232-236(in Chinese).
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[21]Naik C V,Puduppakkam K V,Modak A,et al.Detailed chemical kinetic mechanism for surrogates of alternative jet fuels[J].Combustion and Flame,2011,158(3):434-445.