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<p>The effect of the height of an arc-shaped vortex generator (VG) on the heat transfer performance of a finned-tube heat exchanger (FTHE) was numerically analyzed using ANSYS Fluent with the SST k–ω model. The performance was evaluated by the Nusselt number (Nu) and friction factor (f) at Reynolds numbers (Re) ranging from 1500 to 5000. The VG height was varied from 2.15 mm to 1.29 mm, reducing the heat transfer area. Although this reduction generally decreases Nu, the modified heat transfer mechanism caused by the reduced height led to a slight 1.35 % increase. Meanwhile, f decreased by 30 %, yielding a maximum performance enhancement coefficient (PEC) of 1.22. An artificial neural network (ANN) model and correlation equations accurately predicted Nu and f within 5 %, and these results can be effectively used to determine optimal VG design conditions and to provide reliable predictive tools for enhanced thermal performance.</p>

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Numerical analysis of the heat transfer effect on arc-shaped vortex generator height in a finned-tube heat exchanger

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Jae Hun Choi,
  • Jeong Geun Gwon,
  • Hoon Ki Choi,
  • Yong Gap Park

摘要

The effect of the height of an arc-shaped vortex generator (VG) on the heat transfer performance of a finned-tube heat exchanger (FTHE) was numerically analyzed using ANSYS Fluent with the SST k–ω model. The performance was evaluated by the Nusselt number (Nu) and friction factor (f) at Reynolds numbers (Re) ranging from 1500 to 5000. The VG height was varied from 2.15 mm to 1.29 mm, reducing the heat transfer area. Although this reduction generally decreases Nu, the modified heat transfer mechanism caused by the reduced height led to a slight 1.35 % increase. Meanwhile, f decreased by 30 %, yielding a maximum performance enhancement coefficient (PEC) of 1.22. An artificial neural network (ANN) model and correlation equations accurately predicted Nu and f within 5 %, and these results can be effectively used to determine optimal VG design conditions and to provide reliable predictive tools for enhanced thermal performance.