English(EN) Kriging and neural network models for pressure losses across perforated plates

新的克里金法和神经网络模型预测压损

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-28 22:21

研究人员开发了两种新的数据驱动模型，一种使用克里金法，另一种使用人工神经网络（NN），以预测多孔板湍流中的压损。这些模型在现有实验数据上进行了训练，与传统的经验公式相比，表现出了优越的性能。研究还表明，这些数据驱动的方法可以有效地集成到计算流体动力学模拟中，从而为实际应用产生准确的预测。 AI

影响这些数据驱动的模型为预测湍流中的压损提供了一种更准确、更可行的方法，有可能改进计算流体动力学应用。

排序理由该条目描述了一篇研究论文，提出了预测压损的新模型。[lever_c_demoted from research: ic=1 ai=0.7]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-28 22:21

多孔板压降的克里金和神经网络模型

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 lite…