使用机器学习预测碎波冲击过程中气囊的动力学特性
Predicting the dynamics of a gas pocket during breaking wave impacts using machine learning
AI总结：
本文提出基于卷积长短期记忆的ML模型，利用冲击前两张高速影像预测碎波撞墙形成气囊的六个动力学标量，并在67种波浪状态实验中验证其可准确再现压力极值与振荡频率趋势。
摘要：
我们研究了机器学习模型预测碎波冲击固体墙时形成的气囊动力学特性的可行性与准确性。所提出的ML模型基于卷积长短期记忆结构，并使用实验数据进行训练。具体而言，该模型以冲击前的两张高速相机快照作为输入，并输出六个描述气囊动力学特性的标量。实验在波浪水槽中进行，其中我们利用孤立子并结合测深剖面，在装有动态压力传感器的固体墙附近产生波浪破碎。通过改变水深 $h_\ell$ 和参数 $α=A/h_\ell$（其中 $A$ 为孤立子波幅），我们能够生成一系列独特的碎波，其具有不同的气囊尺寸和波浪运动学特性。在这个所谓的波浪生成相空间（$h_\ell$，$α$）中，我们对67种不同的波浪状态进行了实验，这些状态构成了我们的数据集。在实验方面，我们发现气囊的振荡频率可归因于初始气体体积加上一个几何修正，并且最大和最小压力在定性上能够被一维Bagnold模型很好地捕捉。在ML模型方面，我们将其性能与实验数据进行比较，发现该模型定量再现了实验中发现的趋势，尤其是气囊内最大压力、最小压力以及振荡频率方面的趋势。
英文摘要：
We investigate the feasibility and accuracy of a machine learning model to predict the dynamics of a gas pocket that is formed when a breaking wave impacts on a solid wall. The proposed ML model is based on the convolutional long short-term memory structure and is trained with experimental data. In particular, it takes as input two high-speed camera snapshots before impact and produces as output six scalars that describe the dynamics of the gas pocket. The experiments are performed in a wave flume, where we use solitons -- in combination with a bathymetry profile -- to generate wave breaking close to a solid wall which is instrumented with dynamic pressure sensors. By varying the water depth $h_\ell$ and the parameter $α= A/h_\ell$, where $A$ is the soliton wave amplitude, we are able to generate a family of unique breaking waves with different gas pocket sizes and wave kinematics. In this so-called phase space of wave generation ($h_\ell$, $α$), we perform experiments on 67 different wave states that form our dataset. Experimentally, we find that the frequency of oscillation of the gas pocket can be attributed to the initial volume of gas plus a geometric correction and that the maximum and minimum pressures are qualitatively well captured by the one-dimensional Bagnold model. In terms of the ML model, we compare its performance to the experimental data and find that the model quantitatively reproduces the trends found in the experiments -- in particular for the maximum and minimum pressure in the gas pocket and the frequency of oscillation.
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