This paper reports experimental investigations of drop impacts onto chemically treated surfaces with wettability from 5° to 160°. To follow in time the drop spreading, a high speed video camera was used, and it allows us to determine precisely the expansion of the drop and the profile of the free surface at the contact line. By changing the impact velocity, between less than 0.5 and 5 m/s, and the viscosity, from 1 to 100 mPa s, at constant surface tension, a broad range of Reynolds and Weber numbers is explored. This paper is divided into two parts. In the first part, the experimental drop evolution during spreading is directly reported and compared with previous works. Secondly, the emphasis is on the importance of the apparent dynamic contact angle for the prediction of the maximum spreading diameter. This achievement is manifested at low Reynolds numbers at which the matching between the experiment and the model is improved greatly.

The experimental breakup of liquid jets subjected to a sinusoidal perturbation is investigated in the Rayleigh and first wind-induced regimes. Stroboscopic illumination of the jet and laser photometry method are used. The ability of linear spatial and temporal theories to describe certain aspects of the phenomenon is stressed. A review of data in the literature shows that the limited experimental windows investigated so far do not allow definite conclusions to be drawn. Our results, obtained over a wide range of fluid viscosity and jet velocity values, show that the linear theory of Sterling and Sleicher accurately predicts the variation in breakup length with jet velocity. The exponential character of the initial growth of a monochromatic perturbation along the jet is also described quantitatively. These results were obtained by carefully controlling the initial jet surface perturbation. It is also shown that transient surface tension and jet contraction have to be taken into account to analyze the experimental results.

This paper develops a model for fast filament stretching, thinning, and break-up for Newtonian and non-Newtonian fluids, and the results are compared against experimental data where fast filament relaxation occurs. A 1D approximation was coupled with the arbitrary Lagrangian Eulerian (ALE) formulation to perform simulations that captured both filament thinning and break-up. The modeling accounts for both the initial polymer stretching processes from the precise movement of the two moving pistons and also the subsequent thinning when the pistons are at rest. The simulations were first validated for a low viscosity Newtonian fluid matched to experimental data obtained from a recently developed apparatus, the Cambridge Trimaster. A non-Newtonian polymer fluid, with high frequency linear viscoelastic behavior characterized using a piezoaxial vibrator rheometer, was modeled using both an Oldroyd-B and FENE-CR single-mode constitutive models. The simulations of the filament deformation were compared with experiment. The simulations showed a generally reasonable agreement with both the stretch and subsequent relaxation experimental responses, although the mono mode models used in this paper were unable to capture all of the details for the experimental time evolution relaxation profile of the central filament diameter. V

results on jet stimulated by 302 Abstract We investigate the behaviour of a liquid jet stimulated by pressure disturbances using a photometric measurement of the jet shadow width. Two apparatuses involving lights of different nature are utilized and measurements are taken from the exit of the nozzle to drop breakoff for different operating conditions. Fourier analysis is applied to characterize the spatial evolution of the jet shape. In contrast to previous studies where only amplitudes of the Fourier modes are reported, phase shifts are also recovered for low and high initial perturbations. We show that the spatial reconstruction of the jet from the temporal Fourier analysis at different abscissae is in excellent agreement with the experimental profiles.

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This paper presents a physically based numerical model to simulate droplet impact, spreading, and eventually rebound of a viscoelastic droplet. The simulations were based on the volume of fluid (VOF) method in conjunction with a dynamic contact model accounting for the hysteresis between droplet and substrate. The non‐Newtonian nature of the fluid was handled using FENE‐CR constitutive equations which model a polymeric fluid based on its rheological properties. A comparative simulation was carried out between a Newtonian solvent and a viscoelastic dilute polymer solution droplet. Droplet impact analysis was performed on hydrophilic and superhydrophobic substrates, both exhibiting contact angle hysteresis. The effect of substrates’ wettability on droplet impact dynamics was determined the evolution of the spreading diameter. While the kinematic phase of droplet spreading seemed to be independent of both the substrate and fluid rheology, the recoiling phase seemed highly influenced by those operating parameters. Furthermore, our results implied a critical polymer concentration in solution, between 0.25 and 2.5% of polystyrene (PS), above which droplet rebound from a superhydrophobic substrate could be curbed. The present model could be of particular interest for optimized 2D/3D printing of complex fluids.

Drop impact on a dry substrate is ubiquitous in nature and industrial processes, including aircraft de-icing, ink-jet printing, microfluidics, and additive manufacturing. While the maximum spreading factor is crucial for controlling the efficiency of the majority of these processes, there is currently no comprehensive approach for predicting its value. In contrast to the traditional approach based on scaling laws and/or analytical models, this paper proposes a data-driven approach for estimating the maximum spreading factor using supervised machine learning (ML) algorithms such as linear regression, decision tree, random forest, and gradient boosting. For this purpose, a dataset of hundreds of experimental results from the literature and our own—spanning the last thirty years—is collected and analyzed. The dataset was divided into training and testing sets, each representing 70% and 30% of the input data, respectively. Subsequently, machine learning techniques were applied to relate the maximum spreading factor to relevant features such as flow controlling dimensionless numbers and substrate wettability. In the current study, the gradient boosting regression model, capable of handling structured high-dimensional data, is found to be the best-performing model, with an R2-score of more than 95%. Finally, the ML predictions agree well with the experimental data and are valid across a wide range of impact conditions. This work could pave the way for the development of a universal model for controlling droplet impact, enabling the optimization of a wide variety of industrial applications.

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