An intelligent computing technique for fluid flow problems using hybrid adaptive neural network and genetic algorithm

A new hybrid adaptive neural network (ANN) with modified adaptive smoothing errors (MASE) based on genetic algorithm (GA) employing modified adaptive relaxation (MAR) are presented in this paper to construct learning system for complex problem solving in fluid dynamics. This system can predict an incompressible viscous fluid flow represents by stream function (@j) through symmetrical backward-facing steps channels. The proposed learning system is constructed as an intelligent computing technique by enforcing three stages run simultaneously; the first stage concerns to construct finite-element method (FEM) employing a new approach named modified adaptive incremental loading (MAIL) to build-up in run-time a dataset driven that contains an effective patterns represented by @j for specific Reynolds number (Re), these patterns are associated to three kinds of clusters. The second stage is pertained a new hybrid neural network with new modification of adaptive smoothing errors and the third stage illustrated to modifying the numerical values of neural network connection weights through certain training algorithm with new optimization approach. The present simulation results of the proposed learning system are in good agreement with the available previous works and it is fast enough and stable.

[1]  R. Glowinski,et al.  Partial differential equations : modeling and numerical simulation , 2008 .

[2]  S. Payne.  A model of the interaction between autoregulation and neural activation in the brain. , 2006, Mathematical biosciences.

[3]  Zhenquan Li,et al.  A MASS CONSERVATIVE STREAMLINE TRACKING METHOD FOR THREE-DIMENSIONAL CFD VELOCITY FIELDS , 2002 .

[4]  David E. Goldberg,et al.  Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design , 2005 .

[5]  Régis Duvigneau,et al.  Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD , 2004 .

[6]  Anthony J. Calise,et al.  Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks , 2002, IEEE Trans. Neural Networks.

[7]  B. Karlik,et al.  Flow geometry optimization of channels with baffles using neural network and second law of thermodynamics , 2008 .

[8]  Steven Orla Kimbrough,et al.  Exploring a Two-Population Genetic Algorithm , 2003, GECCO.

[9]  Mustafa Gölcü,et al.  Neural network analysis of head-flow curves in deep well pumps , 2006 .

[10]  Abdel Belaïd,et al.  A Fast Learning Strategy Using Pattern Selection for Feedforward Neural Networks , 2006 .

[11]  Ivan Nunes da Silva,et al.  An approach based on neural networks for estimation and generalization of crossflow filtration processes , 2008, Appl. Soft Comput..

[12]  Dominique Laurence,et al.  Incompressible separated flows simulations with the smoothed particle hydrodynamics gridless method , 2005 .

[13]  Erdinc Eker,et al.  Lattice Boltzmann Simulation of Fluid Flow in Synthetic Fractures , 2006 .

[14]  Irccyn,et al.  Tenth international workshop on frontiers in handwriting recognition , 2006 .

[15]  Marek Wojciechowski,et al.  Artificial Neural Network as a numerical form of effective constitutive law for composites with parametrized and hierarchical microstructure , 2005 .

[16]  Nameer N. El. Emam,et al.  COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION , 2008 .

[17]  EL-Emam,et al.  Computing of Compressible Flow Using Neural Network Based-on Dual-Level Clustering , 2009 .

[18]  Zhenquan Li,et al.  Modelling the Dynamics of Turbulent Floods , 1999, SIAM J. Appl. Math..

[19]  Mingui Sun,et al.  Solving partial differential equations in real-time using artificial neural network signal processing as an alternative to finite-element analysis , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[20]  Ł. Kaczmarczyk,et al.  Neural procedures for the hybrid FEM/NN analysis of elastoplatic plates , 2005 .

[21]  Leon N. Cooper,et al.  Selecting Data for Fast Support Vector Machines Training , 2007, Trends in Neural Computation.