scholarly journals Compound jetting from bubble bursting at an air-oil-water interface

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bingqiang Ji ◽  
Zhengyu Yang ◽  
Jie Feng
Keyword(s):  
Scaling Laws ◽  
Surface Microlayer ◽  
Size Change ◽  
Cavity Collapse ◽  
Bubble Bursting ◽  
Sea Surface Microlayer ◽  
Wide Range ◽  
Aqueous Surface ◽  
Multi Phase ◽  
Tip Radius

AbstractBursting of bubbles at a liquid surface is ubiquitous in a wide range of physical, biological, and geological phenomena, as a key source of aerosol droplets for mass transport across the interface. However, how a structurally complex interface, widely present in nature, mediates the bursting process remains largely unknown. Here, we document the bubble-bursting jet dynamics at an oil-covered aqueous surface, which typifies the sea surface microlayer as well as an oil spill on the ocean. The jet tip radius and velocity are altered with even a thin oil layer, and oily aerosol droplets are produced. We provide evidence that the coupling of oil spreading and cavity collapse dynamics results in a multi-phase jet and the follow-up droplet size change. The oil spreading influences the effective viscous damping, and scaling laws are proposed to quantify the jetting dynamics. Our study not only advances the fundamental understanding of bubble bursting dynamics, but also may shed light on the airborne transmission of organic matters in nature related to aerosol production.

scholarly journals Riding the Plumes: Characterizing Bubble Scavenging Conditions for the Enrichment of the Sea-Surface Microlayer by Transparent Exopolymer Particles

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 454
Author(s):  
Tiera-Brandy Robinson ◽  
Helge-Ansgar Giebel ◽  
Oliver Wurl
Keyword(s):  
Transport Mechanism ◽  
Sea Surface ◽  
Surface Microlayer ◽  
Phytoplankton Species ◽  
Transparent Exopolymer Particles ◽  
Depth Profiles ◽  
Bubble Bursting ◽  
Precursor Material ◽  
Sea Surface Microlayer ◽  
Over Time

Transparent exopolymer particles (TEP) act as a major transport mechanism for organic matter (OM) to the sea surface microlayer (SML) via bubble scavenging, and into the atmosphere via bubble bursting. However; little is known about the effects of bubble scavenging on TEP enrichment in the SML. This study examined the effects of several bubbling conditions and algae species on the enrichment of TEP in the SML. TEP enrichment in the SML was enhanced by bubbling, with a larger impact from bubbling rate than bubble size and increasing enrichment over time. Depth profiles showed that any TEP aggregates formed in the underlying water (ULW) were rapidly (<2 min) transported to the SML, and that TEP was entrained in the SML by bubbling. Species experiments determined that the presence of different phytoplankton species and their subsequent release of precursor material further enhance the effectiveness of TEP enrichment via bubble scavenging.

scholarly journals Jet Dynamics Associated with Drop Impact on Micropillared Substrate

Fluids ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 155
Author(s):  
Brooklyn Asai ◽  
Anayet Ullah Siddique ◽  
Hua Tan
Keyword(s):  
Time Dependence ◽  
Power Law ◽  
High Speed ◽  
Scaling Laws ◽  
Scaling Law ◽  
Droplet Impact ◽  
Dimensionless Time ◽  
Cavity Collapse ◽  
Bubble Bursting ◽  
Jet Breakup

The jetting phenomenon associated with droplet impact upon a hydrophilic micropillared substrate was analyzed in detail using a high-speed camera. Viscosities of the fluids were varied using differing concentrations of glycerol in deionized water. This paper aims to connect similarities between this form of capillary jetting and another well-known jetting phenomenon from the bubble bursting. Both experience a cavity collapse when opposing fluid fronts collide which causes a singularity at the liquid surface, thus leading to the occurrence of jetting. Following processes used to define scaling laws for bubble bursting, a similar approach was taken to derive scaling laws for the dimensionless jet height, jet radius, base height, and radius of the jet base with respect to dimensionless time for the jetting phenomenon associated with the droplet impact. The development of a top droplet before the breakup of the jet also allows the examination of a scaling law for the necking diameter. We find that with the proper scaling factors, the evolution of the jet profile can collapse into a master profile for different fluids and impact velocities. The time dependence of the necking diameter before the jet breakup follows the power law with an exponent of ~2/3. Contrastingly, for other jet parameters such as the radius and height, the power law relationship with time dependence was not found to have a clear pattern that emerged from these studies.

scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

Response : CLOD Spreading in the Sea-Surface Microlayer

Science ◽  
1995 ◽  
Vol 270 (5238) ◽  
pp. 897-898
Author(s):  
Mark M. Littler ◽  
Diane S. Littler
Keyword(s):  
Sea Surface ◽  
Surface Microlayer ◽  
Sea Surface Microlayer

CLOD Spreading in the Sea-Surface Microlayer

Science ◽  
1995 ◽  
Vol 270 (5238) ◽  
pp. 897-897
Author(s):  
M. S. Hale ◽  
J. G. Mitchell
Keyword(s):  
Sea Surface ◽  
Surface Microlayer ◽  
Sea Surface Microlayer

Aerodynamic Similarity Principles and Scaling Laws for Windmilling Fans

2018 ◽  
Author(s):  
Dilip Prasad
Keyword(s):  
Rotational Speed ◽  
Pressure Loss ◽  
Scaling Laws ◽  
Dynamic Pressure ◽  
Stagnation Pressure ◽  
Design Parameters ◽  
Similarity Parameter ◽  
Wide Range ◽  
Simulation Results ◽  
Good Agreement

Windmilling requirements for aircraft engines often define propulsion and airframe design parameters. The present study is focused is on two key quantities of interest during windmill operation: fan rotational speed and stage losses. A model for the rotor exit flow is developed, that serves to bring out a similarity parameter for the fan rotational speed. Furthermore, the model shows that the spanwise flow profiles are independent of the throughflow, being determined solely by the configuration geometry. Interrogation of previous numerical simulations verifies the self-similar nature of the flow. The analysis also demonstrates that the vane inlet dynamic pressure is the appropriate scale for the stagnation pressure loss across the rotor and splitter. Examination of the simulation results for the stator reveals that the flow blockage resulting from the severely negative incidence that occurs at windmill remains constant across a wide range of mass flow rates. For a given throughflow rate, the velocity scale is then shown to be that associated with the unblocked vane exit area, leading naturally to the definition of a dynamic pressure scale for the stator stagnation pressure loss. The proposed scaling procedures for the component losses are applied to the flow configuration of Prasad and Lord (2010). Comparison of simulation results for the rotor-splitter and stator losses determined using these procedures indicates very good agreement. Analogous to the loss scaling, a procedure based on the fan speed similarity parameter is developed to determine the windmill rotational speed and is also found to be in good agreement with engine data. Thus, despite their simplicity, the methods developed here possess sufficient fidelity to be employed in design prediction models for aircraft propulsion systems.

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