Indian Society for Heat and Mass Transfer
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PREDICTION OF POOL BOILING HEAT TRANSFER COEFFICIENTS OF REFRIGERANT R-141b ON NANOCOATED SURFACES USING ARTIFICIAL NEURAL NETWORK

Sandipan Deb
Department of Mechanical Engineering, National Institute of Technology Agartala, Tripura, India

Prasenjit Dey
Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, India

DOI: 10.1615/IHMTC-2017.2320
pages 1673-1677

摘要

The nucleate pool boiling heat transfer on surfaces using refrigerant recently turned out to be more essential on account of the ideal outline of the engineering gadgets and flooded evaporators to preserve energy and conserve the natural resources. Enhancement of heat transfer rate relies on the design of heating surface, sort of refrigerant and operating parameters like heating surface roughness, surface orientation, operating pressure and temperature. The mechanism of pool boiling heat transfer with nano-refrigerants has been examined by many researchers for a long time and depicted the elucidation of characteristics for the fluids. In the present work, the prediction of nucleate pool boiling heat transfer coefficient of silicon dioxide (SiO2) nanocoated thin film (TF) circular flat type copper surfaces have been studied with saturated refrigerant R-141b at atmospheric pressure using Artificial Neural Network (ANN). The ANN methodology was accomplished in Mat-Lab Platform and observed that the prediction efficiency of present ANN structure is near about 100% when it was compared with the experimental results. The mean absolute relative error was calculated to evaluate the error in between prediction data sets and experimental data sets.

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