Three-Dimensional Self-Similarity of Coalescing Viscous Drops in the Thin-Film Regime.

Phys Rev Lett

Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, USA.

Published: September 2022

AI Article Synopsis

Article Abstract

Coalescence and breakup of drops are classic problems in fluid physics that often involve self-similarity and singularity formation. While the coalescence of suspended drops is axisymmetric, the coalescence of drops on a substrate is inherently three-dimensional. Yet, studies so far have only considered this problem in two dimensions. In this Letter, we use interferometry to reveal the three-dimensional shape of the interface as two drops coalescence on a substrate. We unify the known scaling laws in this problem within the thin-film approximation and find a three-dimensional self-similarity that enables us to describe the anisotropic shape of the dynamic interface with a universal curve.

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevLett.129.144501DOI Listing

Publication Analysis

Top Keywords

three-dimensional self-similarity
8
drops
5
three-dimensional
4
self-similarity coalescing
4
coalescing viscous
4
viscous drops
4
drops thin-film
4
thin-film regime
4
coalescence
4
regime coalescence
4

Similar Publications

Kinetics of vapor-liquid and vapor-solid phase separation under gravity.

Soft Matter

January 2025

Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.

We study the kinetics of vapor-liquid and vapor-solid phase separation of a hydrodynamics preserving three-dimensional one-component Lennard Jones system in the presence of an external gravitational field using extensive molecular dynamic simulation. A bicontinuous domain structure is formed when the homogeneous system near the critical density is quenched inside the coexistence region. In the absence of gravity, the domain morphology is statistically self-similar and the length scale grows as per the existing laws.

View Article and Find Full Text PDF

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

J Imaging Inform Med

December 2024

Medical Data Science Academy and College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance.

View Article and Find Full Text PDF

In the acquisition process of 3D cultural relics, it is common to encounter noise. To facilitate the generation of high-quality 3D models, we propose an approach based on graph signal processing that combines color and geometric features to denoise the point cloud. We divide the 3D point cloud into patches based on self-similarity theory and create an appropriate underlying graph with a Markov property.

View Article and Find Full Text PDF

Coal-series diatomite (CSD) is widely distributed in China and has poor functional and structural properties and exhibits limited utilization of high value-added materials, resulting in a serious waste of resources and tremendous pressure on the environment. Moreover, due to differences in the mineralogical characteristics of CSD, different particle size scales (PSSs) have different functional structures and exhibit different self-similarities. In this study, we took CSD as the research object and PSS as the entry point and carried out a self-similarity study based on gas adsorption and an image processing method to illustrate the microstructures and self-similarities of different PSSs.

View Article and Find Full Text PDF

DOVE: Doodled vessel enhancement for photoacoustic angiography super resolution.

Med Image Anal

May 2024

Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China; Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China. Electronic address:

Deep-learning-based super-resolution photoacoustic angiography (PAA) has emerged as a valuable tool for enhancing the resolution of blood vessel images and aiding in disease diagnosis. However, due to the scarcity of training samples, PAA super-resolution models do not generalize well, especially in the challenging in-vivo imaging of organs with deep tissue penetration. Furthermore, prolonged exposure to high laser intensity during the image acquisition process can lead to tissue damage and secondary infections.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!