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煤基喷气燃料代用组分神经网络混合构建方法

刘振涛 许全宏 张弛 霍伟业 林宇震

刘振涛, 许全宏, 张弛, 霍伟业, 林宇震. 煤基喷气燃料代用组分神经网络混合构建方法[J]. 航空动力学报, 2016, 31(11): 2652-2658. doi: 10.13224/j.cnki.jasp.2016.11.012
引用本文: 刘振涛, 许全宏, 张弛, 霍伟业, 林宇震. 煤基喷气燃料代用组分神经网络混合构建方法[J]. 航空动力学报, 2016, 31(11): 2652-2658. doi: 10.13224/j.cnki.jasp.2016.11.012
LIU Zhen-tao, XU Quan-hong, ZHANG Chi, HUO Wei-ye, LIN Yu-zhen. Surrogate formulation methodology of coal-based jet fuel based on neural network mixing model[J]. Journal of Aerospace Power, 2016, 31(11): 2652-2658. doi: 10.13224/j.cnki.jasp.2016.11.012
Citation: LIU Zhen-tao, XU Quan-hong, ZHANG Chi, HUO Wei-ye, LIN Yu-zhen. Surrogate formulation methodology of coal-based jet fuel based on neural network mixing model[J]. Journal of Aerospace Power, 2016, 31(11): 2652-2658. doi: 10.13224/j.cnki.jasp.2016.11.012

煤基喷气燃料代用组分神经网络混合构建方法

doi: 10.13224/j.cnki.jasp.2016.11.012
基金项目: 

国家自然科学基金(51306010);北京市自然科学基金(3152020)

详细信息
    作者简介:

    刘振涛(1993-),男,辽宁瓦房店人,硕士生,主要从事航空替代燃料的研究.

  • 中图分类号: V312.3

Surrogate formulation methodology of coal-based jet fuel based on neural network mixing model

  • 摘要: 为了建立航空燃料的喷雾模型,用于高保真液雾燃烧数值模拟,提出了基于人工神经网络混合模型的煤基喷气燃料代用组分构建方法.基于这一构建方法,重点针对煤基喷气燃料的雾化特性,利用多组分混合燃料的理化性质数据库对神经网络进行训练,获得了混合燃料理化性质隐式预测模型,结合随机投点优化方法,构建出能够很好地模拟煤基喷气燃料目标理化性质的代用组分.结果表明:该代用组分包含了5种碳氢化合物成分,摩尔分数为11.46%正癸烷、23.29%正十二烷、49.87%正十四烷、6.66%异辛烷和8.72%甲基环己烷.通过雾化特性实验,验证了代用组分对真实燃料雾化性能的模拟效果.该代用组分构建方法可以较好地解决混合燃料模拟过程中的非线性问题,通过改变目标理化性质可构建出相应代用组分.

     

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出版历程
  • 收稿日期:  2016-02-19
  • 刊出日期:  2016-11-28

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