Kriging and neural network models for pressure losses across perforated plates
In this paper, two novel data-driven models based on kriging and neural networks (NN) are proposed to predict pressure losses across perforated plates with circular perforations in turbulent flows. The models are developed using two sets of experimental data available in the literature. The predictive performance of the proposed models is assessed and compared against widely used empirical formulae. It is found that the proposed models consistently outperform existing empirical models for most perforated plate configurations contained in the experimental datasets. Besides, the predicted pressure losses generally show good agreement with experimental measurements, demonstrating that data-d...
| Authors | Shuai Li |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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