Hybrid genetic algorithm-self organizing map network for decision support system: An application in combined mode conduction-radiation heat transfer in porous medium

Abstract

Hybrid Genetic Algorithm (GA)-Self organizing map (SOM) network is explored for the first time in determination of regime of operation of heat transfer in porous medium. Combined mode conduction-radiation heat transfer in porous medium is dependent on various key features of porous medium like scattering albedo ω, down-stream porosity ϕ₂, convective coupling P₂, and extinction coefficient β. Depending on the values of these key features, heat transfer in porous medium has 16 different regime of operation. Correct determination of the regime of operation of porous medium, helps in utilization of porous medium to its maximum potential. Different architecture of SOM is employed by GA, and the best is given as output. The best SOM is then used for the determination of regime of operation of porous medium. Two commonly used topologies in SOM: hexagonal and rectangular grids, are explored for the determination of the regime of operation of porous medium. Present approach, aims to simplify the design optimization of porous medium based devices like burners etc.

Keywords:

• Artificial neural network
• conductive and radiative heat transfer
• finite volume method
• genetic algorithm-self organizing map