Prediction of entrance length for low Reynolds number flow in pipe using neuro-fuzzy inference system

Expert Systems with Applications

Volumn 39, Issue 4, 2012, Pages 4545-4557

  Sahu, Mrutyunjaya ^a   Singh, Prashant ^a   Mahapatra, S S ^a   Khatua, K K ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

ANFIS; Development length; Low Reynolds number; Pipe flows

Indexed keywords

ADAPTIVE NETWORK FUZZY INFERENCE SYSTEMS; ANFIS; DATA POINTS; DEVELOPMENT LENGTH; ENTRANCE LENGTH; EXPERIMENTAL DATA; HYBRID-LEARNING ALGORITHM; IN-PIPE; INLET VELOCITY; INPUT PARAMETER; INPUT-OUTPUT DATA; LOW REYNOLDS NUMBER; LOW REYNOLDS NUMBER FLOW; NEURO-FUZZY INFERENCE SYSTEMS; OPTIMAL DISTRIBUTIONS; OUTPUT PARAMETERS; PREDICTIVE MODELS; RULE DATABASE; WORKING CONDITIONS;

COMPUTATIONAL EFFICIENCY; COMPUTATIONAL FLUID DYNAMICS; FORECASTING; FUZZY SYSTEMS; LEARNING ALGORITHMS; MEMBERSHIP FUNCTIONS; PIPE; PIPE FLOW; REYNOLDS NUMBER; VEHICULAR TUNNELS;

FUZZY INFERENCE;

EID: 82255175555     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.09.132     Document Type: Article

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