Feature coefficient prediction of micro-channel based on artificial neural network
作者: Liu HuangWeirong NieXiaofeng WangTeng Shen
作者单位: 1Nanjing University of Science and Technology
刊名: Microsystem Technologies, 2017, Vol.23 (6), pp.2297-2305
来源数据库: Springer Journal
DOI: 10.1007/s00542-016-3067-0
关键词: Particle Swarm OptimizationArtificial Neural NetworkArtificial Neural Network ModelInertial ForceComputational Fluid Dynamic Simulation
英文摘要: In order to study the flow damping in micro-channels, unsteady Bernoulli equation was adopted to derive the motion equation. Artificial neural network (ANN) was adopted to predict the feature coefficient in the motion equation. Firstly, the motion equation of liquid column, flow in micro-channel, under inertial force, was derived. Then, the numerical mapping relationship between the feature parameters and the feature coefficient of micro-channel was modeled using ANN. Moreover, a hybrid optimization algorithm was developed to train the ANN model, which based on back propagation, particle swarm optimization and genetic algorithm. Finally, by taking the rectangular cross section straight micro-channel as an example, the theoretical approach was demonstrated. The training samples were...
原始语种摘要: In order to study the flow damping in micro-channels, unsteady Bernoulli equation was adopted to derive the motion equation. Artificial neural network (ANN) was adopted to predict the feature coefficient in the motion equation. Firstly, the motion equation of liquid column, flow in micro-channel, under inertial force, was derived. Then, the numerical mapping relationship between the feature parameters and the feature coefficient of micro-channel was modeled using ANN. Moreover, a hybrid optimization algorithm was developed to train the ANN model, which based on back propagation, particle swarm optimization and genetic algorithm. Finally, by taking the rectangular cross section straight micro-channel as an example, the theoretical approach was demonstrated. The training samples were...
全文获取路径: Springer  (合作)
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来源刊物:
影响因子:0.827 (2012)

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关键词翻译
关键词翻译
  • coefficient 系数
  • neural 神经系统的
  • micro 
  • artificial 人为的
  • feature 结构元件
  • channel 槽水道
  • motion 运动
  • swarm 
  • computational 计算的
  • training 培养