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New data-driven models predict pressure losses in turbulent flows

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two new data-driven models, one using kriging and the other a neural network (NN), to predict pressure losses in turbulent flows across perforated plates. These models were trained on experimental data and consistently outperformed existing empirical formulas. The NN and kriging models showed good agreement with experimental measurements and are suitable for practical computational fluid dynamics applications when implemented as a source term in RANS equations. AI

IMPACT Introduces novel data-driven approaches for fluid dynamics modeling, potentially improving simulation accuracy.

RANK_REASON Academic paper detailing novel modeling approaches. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New data-driven models predict pressure losses in turbulent flows

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Academic paper detailing novel modeling approaches. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shuai Li ·

    Kriging and neural network models for pressure losses across perforated plates

    arXiv:2606.29628v1 Announce Type: cross Abstract: 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 t…

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