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Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 14 | Page No.: 2501-2518
DOI: 10.3923/jas.2009.2501.2518
 
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Computing of Compressible Flow Using Neural Network Based-on Dual-Level Clustering

Nameer N. EL-Emam and Nadia Y. Yousif

Abstract:
This study presents a new technique based on back-propagation algorithm and Adaptive Neural Networks (ANN) to compute a compressible flow represented by velocity profiles through symmetrical double steps channels. The technique adopts a new clustering algorithm named a Dual-level Clustering (DC), which is based on the speed of flow, the flow deflection and the average of false diffusion error . A learning system of three stages is employed, whose first two stages are run simultaneously while the third works as an optimizer stage. The first stage constructs a finite element analysis employing adaptive incremental loading to select appropriate patterns effectively. The second stage concerns an ANN with Modified Adaptive Smoothing Error (MASE), whereas the third stage is to classify a set of patterns into DC to reduce the effect of errors. The proposed training algorithm is fast enough and the simulation results of the learning system are in harmony with the available previous works. The success of such algorithm can be attributed to three reasons. The first is the employment of ANN that is an excellent approximator, especially if training starts from laminar flow patterns. The second reason is the speed of training due to the use of ANN with learning rate α = 0.7 on the few number of selected patterns equal to 464 and the third reason is satisfying the stability and the accuracy for high range of Reynolds numbers (Re) Re = 4500 due to the use of MASE and DC based on false diffusion error.
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How to cite this article:

Nameer N. EL-Emam and Nadia Y. Yousif, 2009. Computing of Compressible Flow Using Neural Network Based-on Dual-Level Clustering. Journal of Applied Sciences, 9: 2501-2518.

DOI: 10.3923/jas.2009.2501.2518

URL: https://scialert.net/abstract/?doi=jas.2009.2501.2518

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