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The motion of particles through density-stratified interfaces is a commonphenomenon in environmental and engineering applications. However, themechanics of particle-stratification interactions in various combinations ofparticle and fluid properties are not well understood. This study presents anovel machine-learning (ML) approach to experimental data of inertial particlescrossing a density-stratified interface. A simplified particle settlingexperiment was conducted to obtain a large number of particles and expand theparameter range, resulting in an unprecedented data set that has been shared asopen data. Using ML, the study explores new correlations that collapse the datafrom this, and previous work Verso et al. (2019). The ``delay time,'' which isthe time between the particle exiting the interfacial layer and reaching asteady-state velocity, is found to strongly depend on six dimensionlessparameters formulated by ML feature selection. The data shows a correlationbetween the Reynolds and Froude numbers within the range of the experiments,and the best symbolic regression is based on the Froude number only. Thisexperiment provides valuable insights into the behavior of inertial particlesin stratified layers and highlights opportunities for future improvement inpredicting their motion.

Improved Prediction of Settling Behaviour of Solid Particles through Machine Learning Analysis of Experimental Retention Time Data