Data-driven modeling of the aerodynamic deformation and drag for a freely moving drop in the sub-critical Weber number regime

TL;DR

This paper develops a data-driven model using neural networks to accurately predict the deformation and aerodynamic drag of freely moving drops in the sub-critical Weber number regime, based on simulation data.

Contribution

It introduces a novel neural network approach to model drop shape evolution and drag, leveraging modal coefficients from simulations for improved prediction accuracy.

Findings

The model accurately predicts drop shape evolution.

The model reliably estimates aerodynamic drag.

Validation shows excellent agreement with simulation data.

Abstract

Accurate prediction of the dynamics and deformation of freely moving drops is crucial for numerous droplet applications. When the Weber number is finite but below a critical value, the drop deviates from its spherical shape and deforms as it is accelerated by the gas stream. Since aerodynamic drag on the drop depends on its shape oscillation, accurately modeling the drop shape evolution is essential for predicting the drop's velocity and position. In this study, 2D axisymmetric interface-resolved simulations were performed to provide a comprehensive dataset for developing a data-driven model. Parametric simulations were conducted by systematically varying the drop diameter and free-stream velocity, achieving wide ranges of Weber and Reynolds numbers. The instantaneous drop shapes obtained in simulations are characterized by spherical harmonics. Temporal data of the drag and modal coefficients are collected from the simulation data to train a Nonlinear Auto-Regressive models with eXogenous inputs (NARX) neural network model. The overall model consists of two multi-layer perceptron networks, which predict the modal coefficients and the drop drag, respectively. The drop shape can be reconstructed with the predicted modal coefficients. The model predictions are validated against the simulation data in the testing set, showing excellent agreement for the evolutions of both the drop shape and drag.