End depth computation in inverted semicircular channels using ANNs
作者: R.V. RaikarD. Nagesh KumarSubhasish Dey
作者单位: 1Department of Civil Engineering, Indian Institute of Technology, Kharagpur West Bengal 721302, India
2Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, Karnataka, India
刊名: Flow Measurement and Instrumentation, 2004, Vol.15 (5), pp.285-293
来源数据库: Elsevier Journal
DOI: 10.1016/j.flowmeasinst.2004.06.003
关键词: Artificial neural networkOne-dimensional flowOpen channelsSteady flow
原始语种摘要: Abstract(#br)The paper presents the application of artificial neural network (ANN) to determine the end-depth-ratio (EDR) for a smooth inverted semicircular channel in all flow regimes (subcritical and supercritical). The experimental data were used to train and validate the network. In subcritical flow, the end depth is related to the critical depth, and the value of EDR is found to be 0.705 for a critical depth–diameter ratio up to 0.40, which agrees closely with the value of 0.695 given by Dey [Flow Meas. Instrum. 12 (4) (2001) 253]. On the other hand, in supercritical flow, the empirical relationships for EDR and non-dimensional discharge with the non-dimensional streamwise slope of the channel are established.
全文获取路径: Elsevier  (合作)
影响因子:0.971 (2012)

  • semicircular 半圆的
  • inverted 倒置的
  • depth 
  • dimensional 量纲的
  • subcritical 次临界的
  • network 网络
  • supercritical 超临界的
  • streamwise 顺气流方向
  • ratio 
  • empirical 经验的