The motion of particles through density-stratified interfaces is a common phenomenon in environmental and engineering applications. However, the mechanics of particle-stratification interactions in various combinations of particle and fluid properties are not well understood. This study presents a novel machine-learning (ML) approach to experimental data of inertial particles crossing a density-stratified interface. A simplified particle settling experiment was conducted to obtain a large number of particles and expand the parameter range. Using ML, the study explores new correlations that collapse the data gathered in this and in previous work by Verso et al. (2019). The “delay time”, which is the time between the particle exiting the interfacial layer and reaching a steady-state velocity, is found to strongly depend on six dimensionless parameters formulated by ML feature selection. The data shows a correlation between the Reynolds and Froude numbers within the range of the experiments, and the best symbolic regression is based on the Froude number only. This experiment provides valuable insights into the behavior of inertial particles in stratified layers and highlights opportunities for future improvement in predicting their motion.

颗粒通过密度分层界面的运动是环境和工程应用中的常见现象。然而，颗粒和流体特性的各种组合中颗粒-分层相互作用的机制尚不清楚。这项研究提出了一种新颖的机器学习（ML）方法来处理穿过密度分层界面的惯性粒子的实验数据。进行简化的颗粒沉降实验，以获得大量颗粒并扩大参数范围。该研究使用机器学习探索了新的相关性，这些相关性打破了 Verso 等人在本研究和之前的工作中收集的数据。（2019）。“延迟时间”，即粒子离开界面层和达到稳态速度之间的时间，被发现强烈依赖于机器学习特征选择制定的六个无量纲参数。数据显示了实验范围内雷诺数和弗劳德数之间的相关性，并且最佳符号回归仅基于弗劳德数。该实验为分层层中惯性粒子的行为提供了宝贵的见解，并强调了未来改进预测其运动的机会。