An intensive training course has been developed and implemented at the California State University Long Beach based on 8 years of experience in the multiphase flow area with the specific focus on droplet–solid interactions. Due to the rapid development of droplet-based equipment and industrial techniques, numerous industries are concerned with understanding the behavior of droplet dynamics and the characteristics that govern them. The presence and ensuing characteristics of the droplet regimes (spreading, receding, rebounding, and splashing) are heavily dependent on droplet and surface conditions. The effect of surface temperature, surface wettability, impact velocity, droplet shape and volume on droplet impact dynamics, and heat transfer are discussed in this training paper. Droplet impacts on moving solid surfaces and the effects of normal and tangential velocities on droplet dynamics are other topics that are discussed here. Despite the vast amount of studies into the dynamics of droplet impact, there is still much more to be investigated as research has expanded into a myriad of different conditions. However, the current paper is intended as a practical training document and a source of basic information, therefore, the scope is kept sufficiently broad to be of interest to readers from different engineering disciplines.

Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction (fv) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is fv. The LSTM is shown to give accurate estimations of fv, achieving an average error (relative to CFD) in the peak fv of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations.

Suspension plasma spraying (SPS) is an effective technique to enhance the quality of the thermal barrier, wear-resistant, corrosion-resistant, and superhydrophobic coatings. To create the suspension in the SPS technique, nano and sub-micron solid particles are added to a base liquid (typically water or ethanol). Subsequently, by using either a mechanical injection system with a plain orifice or a twin-fluid atomizer (e.g., air-blast or effervescent), the suspension is injected into the high-velocity high-temperature plasma flow. In the present work, we simulate the interactions between the air-blast suspension spray and the plasma crossflow by using a three-dimensional two-way coupled Eulerian–Lagrangian model. Here, the suspension consists of ethanol (85 wt.%) and nickel (15 wt.%). Furthermore, at the standoff distance of 40 mm, a flat substrate is placed. To model the turbulence and the droplet breakup, Reynolds Stress Model (RSM) and Kelvin-Helmholtz Rayleigh-Taylor breakup model are used, respectively. Tracking of the fine particles is continued after suspension’s fragmentation and evaporation, until their deposition on the substrate. In addition, the effects of several parameters such as suspension mass flow rate, spray angle, and injector location on the in-flight behavior of droplets/particles as well as the particle velocity and temperature upon impact are investigated. It is shown that the injector location and the spray angle have a significant influence on the droplet/particle in-flight behavior. If the injector is far from the plasma or the spray angle is too wide, the particle temperature and velocity upon impact decrease considerably.

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Droplet impact on a spinning surface has been observed in different industries and plays an important role in the performance of industrial systems. In the current study, the dynamics of water droplet impact on a hydrophilic spinning disk is investigated. An experimental setup is designed in a way that droplet diameter, impact velocity, disk rotational speed, and location of impact are precisely controlled. While the droplet diameter is fixed in the present study, other mentioned parameters are changed and their effects on the droplet behavior are discussed. High-speed imaging is used to record the droplet dynamics under various operating conditions. It is demonstrated that after impact, droplet spreads on the surface due to a high adhesion between water and the hydrophilic substrate. It is indicated that the wetted area is a function of time, impact velocity, disk rotational speed, and centrifugal acceleration. Furthermore, depending on the mentioned parameters, different phenomena such as rivulet formation, fingering, and detachment of secondary droplet(s) are observed. In the angular direction, in general, the wetted length increases as time passes. However, in the radial direction, the droplet first spreads on the surface and reaches a maximum value, and then recedes until a plateau is attained. At this instant, a bulk of liquid, which is called wave in this study, moves radially outward from the inner boundary of the droplet toward its outer boundary due to the effect of centrifugal force. Once the wave reaches the outer boundary, depending on its size and momentum, fingers or rivulets are formed, and small droplet(s) may detach. The process is analyzed comprehensively, and different empirical correlations for wetted lengths in radial and angular directions, secondary droplet formation, number of fingers, the onset of fingering, and wave velocity are developed.