Modeling of convection heat transfer of supercritical carbon dioxide in a vertical tube at low Reynolds numbers using artificial neural network

International Communications in Heat and Mass Transfer

Volumn 37, Issue 7, 2010, Pages 901-906

  Pesteei, S M ^a   Mehrabi, M ^a  

^a URMIA UNIVERSITY   (Iran)

Author keywords

Convection heat transfer; Group method of data handling (GMDH) type artificial neural network; Supercritical carbon dioxide

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CONVECTION HEAT TRANSFER; EFFECTIVE PARAMETERS; EMPIRICAL DATA; EMPIRICAL RESULTS; GMDH-TYPE NEURAL NETWORKS; GROUP METHOD OF DATA HANDLING; INPUT PARAMETER; LOCAL HEAT TRANSFER COEFFICIENT; LOW REYNOLDS NUMBER; PRIMARY DATA; STATISTICAL CRITERION; SUPERCRITICAL CARBON DIOXIDES; TARGET PARAMETER; TEST SECTIONS; VERTICAL TUBE;

CARBON DIOXIDE; DATA HANDLING; EFFLUENT TREATMENT; HEAT CONVECTION; HEAT EXCHANGERS; MATHEMATICAL MODELS; REYNOLDS NUMBER; SUPERCRITICAL FLUID EXTRACTION; SUPERCRITICAL FLUIDS;

NEURAL NETWORKS;

EID: 77955267946     PISSN: 07351933     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.icheatmasstransfer.2010.05.018     Document Type: Article

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