The present study is focused on determining the most promising set of dimensionless features and the optimal machine learning algorithm that can be employed for data-driven frictional pressure drop estimation of water (single-phase) and air-water mixture (two-phase) flow in micro-finned horizontal tubes. Accordingly, an experimental activity is first conducted, in which the frictional pressure drop of both water and air-water flows, at various flow conditions, is measured. Next, machine learning based pipelines are developed, in which dimensionless parameters are provided as features and the friction factor (for the single-phase case) and the two-phase flow multipliers (for the two-phase case) are considered as the targets. Next, the feature selection procedure is performed, in which the most promising set of features, while employing a benchmark algorithm, is determined. An algorithm optimization procedure is then performed in order to choose the most suitable algorithm (and the corresponding tuning parameters) that lead to the highest possible accuracy. Moreover, the state-of-the-art physical models are implemented and the corresponding accuracy, while being applied to the experimentally obtained dataset, is determined.

It is demonstrated that only 5 dimensionless features are selected (among 23 provided features) in the obtained pipeline developed for the estimation of the two-phase gas multiplier (in the extraction procedure of which, the single-phase friction factors are determined only using the Reynolds number and two geometrical parameters). Therefore, the latter procedures notably reduce the complexity of the model, while providing a higher accuracy (MARD of 6.72% and 7.05% on the training and test sets respectively) compared to the one achieved using the most promising available physical model (MARD of 15.21%). Finally, through implementing the forward feature combination strategy on the optimal pipeline, the contribution of each feature to the achieved accuracy is shown and the trade-off between the model's complexity (number of features) and the obtained accuracy is presented. Thus, the latter step provides the possibility of utilizing an even inferior number of features, while achieving an acceptable accuracy. Moreover, since these pipelines will be made publicly accessible, the implemented models also offer a higher reproducibility and ease of use.

本研究的重点是确定最有前途的无量纲特征集和最佳机器学习算法，这些算法可用于数据驱动的水（单相）和空气-水混合物（两相）流的摩擦压降估算在微翅片水平管中。因此，首先进行实验活动，其中在各种流动条件下测量水和空气-水流的摩擦压降。接下来，开发基于机器学习的管道，其中提供无量纲参数作为特征，并且将摩擦系数（对于单相情况）和两相流量乘数（对于两相情况）作为目标。接下来，执行特征选择过程，其中最有希望的特征集 确定是否采用基准算法。然后执行算法优化过程，以选择导致最高可能精度的最合适算法（和相应的调整参数）。此外，实现了最新的物理模型，并确定了相应的精度，同时将其应用于实验获得的数据集。

结果表明，在获得的用于估算两相气体倍增器的管线中，仅选择了5个无量纲特征（其中23个提供了特征）（在提取过程中，仅使用雷诺数和两个几何参数）。因此，与使用最有希望的可用物理模型（MARD为15.21）实现的过程相比，后一种过程显着降低了模型的复杂性，同时提供了更高的准确性（分别在训练和测试集上的MARD分别为6.72％和7.05％）。 ％）。最后，通过在最佳流水线上实施前向特征组合策略，可以显示每个特征对实现精度的贡献，以及模型之间的权衡。表示复杂度（特征数量）和获得的精度。因此，后面的步骤提供了利用次等特征的可能性，同时实现了可接受的精度。此外，由于这些管道将可以公开访问，因此已实现的模型还具有更高的可重复性和易用性。