Liquid Droplet Impact Over Hydrophobic Mesh Surfaces and Assessment of Weber Number Dependent Characteristics

Impacting droplets and droplet ejection from hydrophobic mesh surfaces have interest in biomedicine, heat transfer engineering, and self-cleaning of surfaces. The rate and the size of newborn droplets can vary depending on, the droplet fluid properties, Weber number, mesh geometry, and surface wetting states. In the present study, impacting water droplets onto hydrophobic mesh surface is investigated and impact properties including, spreading, rebounding, and droplet fluid penetration and ejection rates are examined. Droplet behavior is assessed using high recording facilities and predicted in line with the experiments. The findings reveal that the critical Weber number for droplet fluid penetrating/ejecting from mesh screen mainly depends on the droplet fluid capillary length, and hydrophobic mesh size. The contact time of impacting droplet over mesh surface reduces with increasing droplet Weber number, which opposes the case observed for impacting droplets over flat hydrophobic surfaces. The restitution coefficient attains lower values for impacting droplets over mesh surfaces than that of flat surfaces. The rate and diameter of the ejected droplet from the mesh increases as droplet Weber increases. At the onset of impact, streamline curvature is formed inside droplet fluid, which creates a stagnation zone with radially varying pressure at the droplet fluid mesh interface. This reduces the ejected droplet diameter from mesh cells as mesh cells are located away from the impacting vertical axis.

Impacting Droplet Can Mitigate Dust from PDMS Micro-Post Array Surfaces

In this paper, the impact mechanisms of a water droplet on hydrophobized micro-post array surfaces are examined and the influence of micro-post arrays spacing on the droplet behavior in terms of spreading, retraction, and rebounding is investigated. Impacting droplet behavior was recorded using a high-speed facility and flow generated in the droplet fluid was simulated in 3D geometry accommodating conditions of the experiments. Micro-post arrays were initially formed lithographically on silicon wafer surfaces and, later, replicated by polydimethylsiloxane (PDMS). The replicated micro-post arrays surfaces were hydrophobized through coating by functionalized nano-silica particles. Hydrophobized surfaces result in a contact angle of 153° ± 3° with a hysteresis of 3° ± 1°. The predictions of the temporal behavior of droplet wetting diameter during spreading agree with the experimental data. Increasing micro-post arrays spacing reduces the maximum spreading diameter on the surface; in this case, droplet fluid penetrated micro-posts spacing creates a pinning effect while lowering droplet kinetic energy during the spreading cycle. Flow circulation results inside the droplet fluid in the edge region of the droplet during the spreading period; however, opposing flow occurs from the outer region towards the droplet center during the retraction cycle. This creates a stagnation zone in the central region of the droplet, which extends towards the droplet surface onset of droplet rebounding. Impacting droplet mitigates dust from hydrophobized micro-post array surfaces, and increasing droplet Weber number increases the area of dust mitigated from micro-post arrays surfaces.

Effect of Surface Structure Complexity on Interfacial Droplet Behavior of Superhydrophobic Titanium Surfaces for Robust Dropwise Condensation

In general, the dropwise condensation supported by superhydrophobic surfaces results in enhanced heat transfer relative to condensation on normal surfaces. However, in supersaturated environments that exceed a certain supersaturation threshold, moisture penetrates the surface structures and results in attached condensation, which reduces the condensation heat transfer efficiency. Therefore, when designing superhydrophobic surfaces for condensers, the surface structure must be resistant to attached condensation in supersaturated conditions. The gap size and complexity of the micro/nanoscale surface structure are the main factors that can be controlled to maintain water repellency in supersaturated environments. In this study, the condensation heat exchange performance was characterized for three different superhydrophobic titanium surface structures via droplet behavior (DB) mapping to evaluate their suitability for power plant condensers. In addition, it was demonstrated that increasing the surface structure complexity increases the versatility of the titanium surfaces by extending the window for improved heat exchange performance. This study demonstrates the usefulness of DB mapping for evaluating the performance of superhydrophobic surfaces regarding their applicability for industrial condenser systems.

A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types.

LATTICE BOLTZMANN PSEUDO-POTENTIAL MODELLING OF MULTIPHASE DROPLET PHENOMENA

The multiphase modeling of a droplet in a multiphase system is considered becoming a fundamental problem in fluid dynamics. A complex understanding of droplet behavior is critical to reveal a deeper insight into a more complex multiphase system. Droplet behavior studies are necessary to obtain a better understanding of solving multiphase problems in both the science and industrial aspect. The droplet behavior is characterized by a non-dimensional number such as the Eötvös number. In this study, numerical simulation was performed using the Lattice Boltzmann method. Parametric studies of Eötvös number was done. The parametric study of the Eo number is obtained using LBM. Based on the results obtained, it is concluded that the gravitational force influences the downwards rate of the droplet. Furthermore, the shape of the droplet during falling was depended on the Eo number as well. The higher Eo number means higher gravitational force, hence the velocity of the droplet is increasing as well as the reaction force of surface tension. This study is beneficial to give a deeper explanation of multiphase phenomena as well as contribute to the modeling of multiphase phenomena using an alternative numerical method of LBM.