标题： 使用机器学习预测破浪冲击过程中气泡的动态 显示英文标题

标题： Predicting the dynamics of a gas pocket during breaking wave impacts using machine learning

摘要： 我们研究了机器学习模型预测当破碎波撞击固体壁时形成的气泡动力学的可行性和准确性。 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，它以前冲击前的两个高速摄像机快照作为输入，并输出六个标量，这些标量描述了气泡的动力学特性。 实验是在波浪水槽中进行的，我们使用孤立波——结合地形剖面——在靠近装有动态压力传感器的固体壁处产生波浪破碎。 通过改变水深$h_\ell$和参数$\alpha = A/h_\ell$，其中$A$是孤立波振幅，我们能够生成一系列具有不同气泡大小和波浪运动的独特破碎波。 在这个所谓的波浪生成相空间（$h_\ell$，$\alpha$）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。 实验上，我们发现气泡的振荡频率可以归因于初始气体体积加上几何修正，而最大和最小压力在定性上被一维Bagnold模型很好地捕捉到了。 在ML模型方面，我们将它的性能与实验数据进行比较，发现该模型定量地再现了实验中发现的趋势——特别是气泡中的最大和最小压力以及振荡频率。 显示英文摘要

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 $\alpha = A/h_\ell$, 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 ($h_\ell$, $\alpha$), 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.