Adaptive mesh axi-symmetric simulation of droplet impact with a spherical particle in mid-air

Yoon, Ikroh; Chergui, Jalel; Jurić, Damir; Shin, Seungwon

doi:10.1016/j.ijmultiphaseflow.2022.104193

Cited by 8 publications

(4 citation statements)

References 79 publications

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“…A two-dimensional axi-symmetric simulation is considered in order to reduce the required numerical resources. Note that the droplet impact behavior would be essentially identical to that from full three-dimensional simulations in the low Weber number regime [57], since there are no meaningful changes in flow structure in the circumferential direction (e.g., cusps or fingering [11][12]), which are generally observed during collisions at high Weber number (e.g., splashing phenomena). The lengths of the simulation domain in the axial (z) and radial (r) directions are set as ZL = 7.5Dd and RL = 10Dd, respectively, where Dd denotes the diameter of the droplet.…”

Section: A Numerical Methodsmentioning

confidence: 89%

Energetics of spreading droplets and role of capillary waves at low Weber numbers below 10

et al. 2023

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In this study, we investigate the energy conversion and dissipation mechanisms of spreading droplets on a solid surface at a low Weber number regime, which neither conventional energy-balance-based theories nor empirical scaling laws can completely explain. The energetic analysis presented in this study shows that on a hydrophilic surface, the actual primary energy source driving the spreading process is the initial surface energy not the initial kinetic energy. The conventional energy-balance-based approaches are found to be valid only for the spreading process on a hydrophobic surface. Particular attention is also paid to the roles of the capillary waves. The capillary waves are found to play significant roles in all of the important flow physics, i.e., the interfacial structure, the oscillatory motions and the rapid collapse of the liquid film, the onset of the viscous regime, and the energy loss mechanism. It is also shown that the energy dissipation caused by the capillary-wave-induced phenomena can be estimated to be 25-35% and 55-65% of the total energy loss for a hydrophilic and a hydrophobic surface, respectively, at the low Weber number regime.

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Section: A Numerical Methodsmentioning

confidence: 89%

Energetics of spreading droplets and role of capillary waves at low Weber numbers below 10

et al. 2023

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“…Since this paper focuses on understanding the physical behavior of a droplet colliding with a particle and since we have used the same numerical methods as in our previous studies 42,47 here we briefly introduce the numerical formulations used in the current study. For more algorithm details and pertinent information on the numerical methods, readers can refer to the appendix and our previous papers.…”

Section: Methodsmentioning

confidence: 99%

“…For more algorithm details and pertinent information on the numerical methods, readers can refer to the appendix and our previous papers. 42,[47][48][49] Note that our simulation methods have been extensively applied to many droplet impact problems with various solid surfaces, e.g., flat surfaces, 50 cylindrical objects, 51 spherical targets, 29,37,42,47 and liquid pools. 52 As a single field formulation, the governing equations are applied to all phases (gas, liquid, and solid) and are solved on the fixed Eulerian grid for incompressible flows:…”

Section: Methodsmentioning

confidence: 99%

“…The simulations have been efficiently performed with the help of a simple adaptive mesh refinement (AMR) technique. 47 From those simulation cases, 2600 data samples for input features (We, Oh, θeqi, and Ω) and during the learning process, by minimizing the loss function using the back propagation algorithm. 68 In the present study, the mean square error (MSE) is defined and applied as a loss function: (8) Where M denotes the number of data samples and β * max(p) denotes the predicted value of the maximum spreading diameter using the deep neural network.…”

Section: Data-driven Prediction Model For the Maximum Spreading Diametermentioning

confidence: 99%

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Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model

et al. 2022

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In the present study, the maximum spreading diameter of a droplet impacting with a spherical particle is numerically studied for a wide range of impact conditions: Weber number (We) 0-110, Ohnesorge number (Oh) 0.0013-0.7869, equilibrium contact angle ( θeqi) 20{degree sign}-160{degree sign}, and droplet-to-particle size ratio (Ω) 1/10-1/2. A total of 2600 collision cases are simulated to enable a systematic analysis and prepare a large dataset for training of a data-driven prediction model. The effects of four impact parameters (We, Oh, θeqi, and Ω) on the maximum spreading diameter ( β*max) are comprehensively analyzed, and particular attention is paid to the difference of β*max between the low and high Weber number regimes. A universal model for prediction of β*max, as a function of We, Oh, θeqi, and Ω, is also proposed based on a deep neural network. It is shown that our data-driven model can predict the maximum spreading diameter well, showing an excellent agreement with the existing experimental results as well as our simulation dataset within a deviation range of {plus minus} 10%.

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Numerical investigation of spreading time in droplet impact with a large spherical surface: from physical analysis to data-driven prediction model

Yoon,

Shin,

Juric

et al. 2024

Theor. Comput. Fluid Dyn.

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Adaptive mesh axi-symmetric simulation of droplet impact with a spherical particle in mid-air

Cited by 8 publications

References 79 publications

Energetics of spreading droplets and role of capillary waves at low Weber numbers below 10

Energetics of spreading droplets and role of capillary waves at low Weber numbers below 10

Maximum spreading of droplet-particle collision covering a low Weber number regime and data-driven prediction model

Numerical investigation of spreading time in droplet impact with a large spherical surface: from physical analysis to data-driven prediction model

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