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<p>Heat transfer enhancement techniques play a crucial role in improving the thermal efficiency of heat exchangers while minimizing energy usage and operational expenses. In this study, the thermo-hydraulic performance of a circular tube equipped with wire-coil inserts and operating with graphene oxide (GO) nanofluid was predicted using an artificial neural network (ANN) model. Experiments were conducted over a Reynolds number range of 5000–18,000 using GO nanofluid with weight concentrations of 0.025–0.1 wt% and wire-coil inserts having pitch-to-diameter ratios (P/D) of 1, 1.5, and 2. The experimental findings revealed that the combined use of GO nanofluid and wire-coil inserts significantly enhanced the Nusselt number, although it also resulted in an increase in the friction factor. At a GO concentration of 0.05 weight% and a P/D ratio of 1.5, the maximum Thermal Performance Factor of 1.21 was attained, signifying an ideal thermo-hydraulic balance. A feed-forward backpropagation ANN model was created with the Reynolds number, nanoparticle concentration, and P/D ratio as input parameters and the Nusselt number and friction factor as outputs in order to forecast system performance. The correctness and dependability of the suggested model were confirmed by the ANN predictions having outstanding agreement with the experimental findings.</p>

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Experimental investigation and ANN modeling of thermo-hydraulic characteristics of GO nanofluid in a tube equipped with wire-coil inserts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Ravindra Gupta,
  • Priyanka Shah,
  • Arvind Mohite,
  • Nagendra Prasad Pandey,
  • Rajkumari Soni,
  • Niraj Tevar,
  • Ankita Sharma,
  • Hardik Solanki

摘要

Heat transfer enhancement techniques play a crucial role in improving the thermal efficiency of heat exchangers while minimizing energy usage and operational expenses. In this study, the thermo-hydraulic performance of a circular tube equipped with wire-coil inserts and operating with graphene oxide (GO) nanofluid was predicted using an artificial neural network (ANN) model. Experiments were conducted over a Reynolds number range of 5000–18,000 using GO nanofluid with weight concentrations of 0.025–0.1 wt% and wire-coil inserts having pitch-to-diameter ratios (P/D) of 1, 1.5, and 2. The experimental findings revealed that the combined use of GO nanofluid and wire-coil inserts significantly enhanced the Nusselt number, although it also resulted in an increase in the friction factor. At a GO concentration of 0.05 weight% and a P/D ratio of 1.5, the maximum Thermal Performance Factor of 1.21 was attained, signifying an ideal thermo-hydraulic balance. A feed-forward backpropagation ANN model was created with the Reynolds number, nanoparticle concentration, and P/D ratio as input parameters and the Nusselt number and friction factor as outputs in order to forecast system performance. The correctness and dependability of the suggested model were confirmed by the ANN predictions having outstanding agreement with the experimental findings.