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

Ansari, Mohammad Reza; Sahu, Kunja Bihari; Chaudhuri, Sumanta; J, Srikanth; Sahu, Debjyoti; Mishra, Vijay Kumar

doi:10.1080/10407790.2023.2216873

Cited by 2 publications

(2 citation statements)

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“…[19][20][21][22][23][24] In contrast to free flame-based conventional burners, a PCM-based burner offers various advantages such as high rate of heat transfer, low exit gas temperature, and lower maximum temperature. 25,26 These features of PCM-based burners are due to the presence of conduction and radiation in the PCM. The performance of PCM-based burners depends on certain attributes of a PCM, such as convective coupling (P 2 ), extinction coefficient (β), downstream porosity (ϕ 2 ), and scattering albedo (ω).…”

Section: Introductionmentioning

confidence: 99%

“…Use of a porous ceramic matrix (PCM)‐based burner is a promising technology in combustion 19–24 . In contrast to free flame‐based conventional burners, a PCM‐based burner offers various advantages such as high rate of heat transfer, low exit gas temperature, and lower maximum temperature 25,26 . These features of PCM‐based burners are due to the presence of conduction and radiation in the PCM.…”

Section: Introductionmentioning

confidence: 99%

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Decision support system based on a hybrid genetic algorithm–Kohonen map for combined mode conduction–radiation heat transfer in a porous medium: A comparative assessment of three variations of the Kohonen map

Ansari,

Mishra,

Sahu

et al. 2024

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A hybrid genetic algorithm (GA)–Kohonen map, with its three variants, is explored for the first time for the decision‐making system in a porous ceramic matrix (PCM)‐based burner through determination of the regime of operation. Four different attributes of PCMs such as convective coupling (P2), extinction coefficient (β), downstream porosity (ϕ2), and scattering albedo (ω) are selected for determining the regime of operation of a PCM‐based burner. Changes in any of these attributes of a PCM lead to significant changes in the temperature profiles of the gas and solid phases. Temperature profiles of the gas and solid phases are computed by developing a numerical model. Various samples corresponding to different regimes are generated and used in a hybrid GA–Kohonen map. The best architectural details such as the neuron number and training epochs are obtained from GA as output. The best Kohonen map is trained with the input data, and regimes of operation for new temperature profiles are predicted. A supervised Kohonen map is able to provide the highest average class prediction of more than 40%. All the variants are assessed under two different types of neuron grids: hexagonal and rectangular. Comparative assessments of the three different variants of Kohonen maps, in terms of CPU time and average class prediction, are carried out.

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