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arXiv 2404.06802physics.flu-dyn
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利用物理信息神经网络识别挟沙水流的沉降速度

Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows

Mickaël Delcey, Yoann Cheny, Jean-Baptiste Keck, Adrien Gans, Sébastien Kiesgen De Richter

AI总结:

本文将PINNs用于盐水上覆挟沙淡水的耦合输运问题,在浓度场或速度场两类训练数据下推断无量纲沉降速度并重建水动力场,且验证了其抗噪鲁棒性。

摘要:

物理信息神经网络(PINNs)在流体动力学模拟领域展现出巨大潜力,尤其是在重建流场和识别关键参数方面。本研究探索应用PINNs恢复沉降流的无量纲沉降速度。该流动涉及上覆于盐水之上的挟沙淡水,由Navier-Stokes方程与泥沙浓度及盐度输运方程耦合描述。研究考察了两种情形:一种训练数据包含盐度和泥沙浓度场,另一种包含速度场。对于这两种情形,我们研究了若干流态,并表明该模型能够推断未知参数并重建流动的水动力场。通过与高保真半拉格朗日求解器的数值模拟进行比较,评估了模型推断的质量。我们通过使用受不同幅度噪声破坏的数据进行训练,证明了模型对噪声的鲁棒性,凸显了PINNs在实际应用中的潜力。

英文摘要:

Physics-Informed Neural Networks (PINNs) have shown great potential in the context of fluid dynamics simulations, particularly in reconstructing flow fields and identifying key parameters. In this study, we explore the application of PINNs to recover the dimensionless settling velocity for sedimentation flow. The flow involves sediment-laden fresh water overlying salt water, which is described by Navier-Stokes equations coupled with sediment concentration and salinity transport equations. Two cases are investigated: one where the training data contains the salinity and sediment concentration fields, and another where it contains the velocity field. For both cases, we investigate several flow regimes and show that the model is capable of inferring the unknown parameter and reconstructing the hydrodynamic field of the flow. The quality of the model inference is assessed by comparing it with numerical simulations from a high-fidelity semi-Lagrangian solver. We demonstrate the model's robustness to noise by training it with data corrupted by noise of varying magnitudes, highlighting the potential of PINNs for real-world applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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