The heat transfer, flow resistance and entropy generation characteristics of micro-fin helical coil tubes (MFHCTs) are investigated numerically. MFHCT with different fin numbers ($2\le N\le 6$), coil pitches ($150mm\le P\le 450mm)$, coil diameters ($600mm\le D\le 1200mm$) and Reynolds numbers ($10945\le Re\le 30845$) are examined. The effects of these geometric parameters on the Nusselt number ($Nu$), friction factor ($f$) and improved entropy generation number ($N{s}^{\prime }$) are discussed. The performance of MFHCT is then compared to that of a smooth helical coil tube (SHCT). The results show that MFCHT always performs better than SHCT, especially in the lower Reynolds number region. Moreover, artificial neural networks (ANNs) are established to predict $Nu$, $f$ and $N{s}^{\prime }$, which are trained by simulation data. This model fits the simulation results better than the multiple linear regression, and the maximum error is no greater than 8%. With the prediction of the network, the micro-fin helical coil tubes are optimized by the entropy minimization method and NSGA-III algorithm. Through optimization, the distribution of design variables is examined. The results demonstrate that a higher Reynolds number and a larger coil diameter and coil pitch lead to a better performance. Additionally, the optimal Pareto points can be utilized to guide the design and operation conditions of micro-fin helical coil tubes.

数值研究了微翅螺旋盘管（MFHCTs）的传热、流动阻力和熵产生特性。MFHCT 具有不同的鳍片数 ($2\le N\le 6$), 线圈节距 ($150米米\le 磷\le 450米米)$, 线圈直径 ($600米米\le D\le 1200米米$) 和雷诺数 ($10945\le \mathrm{电阻}\mathrm{电子}\le 30845$) 进行检查。这些几何参数对努塞尔数的影响 ($N你$), 摩擦系数 ($F$) 和改进的熵生成数 ($N{秒}^{\prime }$) 进行了讨论。然后将 MFHCT 的性能与光滑螺旋盘管 (SHCT) 的性能进行比较。结果表明 MFCHT 总是比 SHCT 表现更好，尤其是在较低雷诺数区域。此外，还建立了人工神经网络 (ANN) 来预测$N你$, $F$ 和 $N{秒}^{\prime }$，它们是由模拟数据训练的。该模型对仿真结果的拟合优于多元线性回归，最大误差不大于8%。通过网络的预测，利用熵最小化方法和NSGA-III算法对微翅螺旋盘管进行优化。通过优化，可以检查设计变量的分布。结果表明，更高的雷诺数和更大的线圈直径和线圈节距导致更好的性能。此外，可以利用最优帕累托点来指导微翅螺旋盘管的设计和运行条件。