Abstract

Present study provides an experimental investigation of the exergetic efficiency due to the flow and heat transfer of nanofluids in different geometries and flow regimes of the double pipe heat exchangers. The experiments with different Geometrical Progression Ratio (GPR) of twists as the new modified twisted tapes and different nanofluid concentration were performed under similar operation condition. Pitch length of the proposed twisted tapes and consequently the twist ratios changed along the twists with respect to the Geometrical Progression Ratio (GPR) whether reducer (RGPR 1). Regarding the experimental data, utilization of RGPR twists together with nanofluids tends to increase exergetic efficiency. Since the Prediction of exergetic efficiency from experimental process is complex and time consuming, artificial neural networks for identification of the relationship, which may exist between the thermal and flow parameters and exergetic efficiency, have been utilized. The network input consists of five parameters \( (\text{Re} ,\Pr ,\varphi , {\rm Tr}, {\rm GPR}) \) that crucially dominate the heat transfer process. The results proved that the introduced ANN model is reliable and capable in proposing a proper development plan for a heat exchanger and/or to determine the optimal plan of operation for heat transfer process.

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