Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Statistical equilibrium of bubble oscillations in dilute bubbly flows.

ABSTRACT: The problem of predicting the moments of the distribution of bubble radius in bubbly flows is considered. The particular case where bubble oscillations occur due to a rapid (impulsive or step change) change in pressure is analyzed, and it is mathematically shown that in this case, inviscid bubble oscillations reach a stationary statistical equilibrium, whereby phase cancellations among bubbles with different sizes lead to time-invariant values of the statistics. It is also shown that at statistical equilibrium, moments of the bubble radius may be computed using the period-averaged bubble radius in place of the instantaneous one. For sufficiently broad distributions of bubble equilibrium (or initial) radius, it is demonstrated that bubble statistics reach equilibrium on a time scale that is fast compared to physical damping of bubble oscillations due to viscosity, heat transfer, and liquid compressibility. The period-averaged bubble radius may then be used to predict the slow changes in the moments caused by the damping. A benefit is that period averaging gives a much smoother integrand, and accurate statistics can be obtained by tracking as few as five bubbles from the broad distribution. The period-averaged formula may therefore prove useful in reducing computational effort in models of dilute bubbly flow wherein bubbles are forced by shock waves or other rapid pressure changes, for which, at present, the strong effects caused by a distribution in bubble size can only be accurately predicted by tracking thousands of bubbles. Some challenges associated with extending the results to more general (nonimpulsive) forcing and strong two-way coupled bubbly flows are briefly discussed.

SUBMITTER: Colonius T

PROVIDER: S-EPMC2698283 | biostudies-other | 2008 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Strong Transient Flows Generated by Thermoplasmonic Bubble Nucleation.

Project description:The challenge of inducing and controlling localized fluid flows for generic force actuation and for achieving efficient mass transport in microfluidics is key to the development of next-generation miniaturized systems for chemistry and life sciences. Here we demonstrate a methodology for the robust generation and precise quantification of extremely strong flow transients driven by vapor bubble nucleation on spatially isolated plasmonic nanoantennas excited by light. The system is capable of producing peak flow speeds of the order mm/s at modulation rates up to ∼100 Hz in water, thus allowing for a variety of high-throughput applications. Analysis of flow dynamics and fluid viscosity dependence indicates that the transient originates in the rapid bubble expansion that follows nucleation rather than being strictly thermocapillary in nature.

| S-EPMC7760215 | biostudies-literature

Efficient manipulation of microparticles in bubble streaming flows.

Project description:Oscillating microbubbles of radius 20-100 μm driven by ultrasound initiate a steady streaming flow around the bubbles. In such flows, microparticles of even smaller sizes (radius 1-5 μm) exhibit size-dependent behaviors: particles of different sizes follow different characteristic trajectories despite density-matching. Adjusting the relative strengths of the streaming flow and a superimposed Poiseuille flow allows for a simple tuning of particle behavior, separating the trajectories of particles with a size resolution on the order of 1 μm. Selective trapping, accumulation, and release of particles can be achieved. We show here how to design bubble microfluidic devices that use these concepts to filter, enrich, and preconcentrate particles of selected sizes, either by concentrating them in discrete clusters (localized both stream- and spanwise) or by forcing them into narrow, continuous trajectory bundles of strong spanwise localization.

| S-EPMC3365321 | biostudies-other

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows.

Project description:This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate's architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints. The hierarchical attribute has two advantages: (i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; (ii) It accelerates the prediction step by enabling adaptivity as the surrogate's evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-[Formula: see text] scenarios, the proposed ML framework can adaptively predict the dynamics of almost thirty species with a maximum relative error of 4.5% for a wide range of initial conditions. Furthermore, when employed in 1-[Formula: see text] shock simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to conventional implicit schemes employed in an operator-splitting integration framework. Given the results presented in the paper, this work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena in multi-dimensional computational fluid dynamics simulations.

| S-EPMC10509218 | biostudies-literature

Statistical properties of autonomous flows in 2D active nematics.

Project description:We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil-water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the relative microscopic sliding of adjacent microtubules, which in turn generates macroscale autonomous flows and chaotic dynamics. We use particle image velocimetry to quantify two-dimensional flows of active nematics and extract their statistical properties. In agreement with the hydrodynamic theory, we find that the vortex areas comprising the chaotic flows are exponentially distributed, which allows us to extract the characteristic system length scale. We probe the dependence of this length scale on the ATP concentration, which is the experimental knob that tunes the magnitude of the active stress. Our data suggest a possible mapping between the ATP concentration and the active stress that is based on the Michaelis-Menten kinetics that governs the motion of individual kinesin motors.

| S-EPMC6924514 | biostudies-literature

Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows.

Project description:While investigating multiphase flows experimentally, the spatiotemporal variation in the interfacial shape between different phases must be measured to analyze the transport phenomena. For this, numerous image processing techniques have been proposed, showing good performance. However, they require trial-and-error optimization of thresholding parameters, which are not universal for all experimental conditions; thus, their accuracy is highly dependent on human experience, and the overall processing cost is high. Motivated by the remarkable improvements in deep learning-based image processing, we trained the Mask R-CNN to develop an automated bubble detection and mask extraction tool that works universally in gas-liquid two-phase flows. The training dataset was rigorously optimized to improve the model performance and delay overfitting with a finite amount of data. The range of detectable bubble size (particularly smaller bubbles) could be extended using a customized weighted loss function. Validation with different bubbly flows yields promising results, with AP50 reaching 98%. Even while testing with bubble-swarm flows not included in the training set, the model detects more than 95% of the bubbles, which is equivalent or superior to conventional image processing methods. The pure processing speed for mask extraction is more than twice as fast as conventional approaches, even without counting the time required for tedious threshold parameter tuning. The present bubble detection and mask extraction tool is available online ( https://github.com/ywflow/BubMask ).

| S-EPMC8076184 | biostudies-literature

Fundraising and vote distribution: A non-equilibrium statistical approach.

Project description:The number of votes correlates strongly with the money spent in a campaign, but the relation between the two is not straightforward. Among other factors, the output of a ballot depends on the number of candidates, voters, and available resources. Here, we develop a conceptual framework based on Shannon entropy maximization and Superstatistics to establish a relation between the distributions of money spent by candidates and their votes. By establishing such a relation, we provide a tool to predict the outcome of a ballot and to alert for possible misconduct either in the report of fundraising and spending of campaigns or on vote counting. As an example, we consider real data from two proportional elections with more than 6000 candidates each, where a detailed data verification is virtually impossible, and show that the number of potential misconducting candidates to audit can be reduced to less than ten.

| S-EPMC6821074 | biostudies-literature

Bayesian statistical analysis of circadian oscillations in fibroblasts.

Project description:Precise determination of a noisy biological oscillator's period from limited experimental data can be challenging. The common practice is to calculate a single number (a point estimate) for the period of a particular time course. Uncertainty is inherent in any statistical estimator applied to noisy data, so our confidence in such point estimates depends on the quality and quantity of the data. Ideally, a period estimation method should both produce an accurate point estimate of the period and measure the uncertainty in that point estimate. A variety of period estimation methods are known, but few assess the uncertainty of the estimates, and a measure of uncertainty is rarely reported in the experimental literature. We compare the accuracy of point estimates using six common methods, only one of which can also produce uncertainty measures. We then illustrate the advantages of a new Bayesian method for estimating period, which outperforms the other six methods in accuracy of point estimates for simulated data and also provides a measure of uncertainty. We apply this method to analyze circadian oscillations of gene expression in individual mouse fibroblast cells and compute the number of cells and sampling duration required to reduce the uncertainty in period estimates to a desired level. This analysis indicates that, due to the stochastic variability of noisy intracellular oscillators, achieving a narrow margin of error can require an impractically large number of cells. In addition, we use a hierarchical model to determine the distribution of intrinsic cell periods, thereby separating the variability due to stochastic gene expression within each cell from the variability in period across the population of cells.

| S-EPMC3478438 | biostudies-literature

Laboratory experiments reveal intrinsic self-sustained oscillations in ocean relevant rotating fluid flows.

Project description:Several ocean Western Boundary Currents (WBCs) encounter a lateral gap along their path. Examples are the Kuroshio Current penetrating into the South China Sea through the Luzon Strait and the Gulf of Mexico Loop Current leaping from the Yucatan peninsula to Florida as part of the Gulf Stream system. Here, we present results on WBC relevant flows, generated in the world's largest rotating platform, where the Earth's sphericity necessary to support WBCs is realized by an equivalent topographic effect. The fluid is put in motion by a pump system, which produces a current that is stationary far from the gap. When the jet reaches the gap entrance, time-dependent patterns with complex spatial structures appear, with the jet leaking, leaping or looping through the gap. The occurrence of these intrinsic self-sustained periodic or aperiodic oscillations depending on current intensity is well known in nonlinear dynamical systems theory and occurs in many real systems. It has been observed here for the first time in real rotating fluid flows and is thought to be highly relevant to explain low-frequency variability in ocean WBCs.

| S-EPMC8792033 | biostudies-literature

Analysis of long-term statistical data of cobalt flows in the EU.

Project description:Long-term statistical data was explored, acquired, processed, and analysed in order to assess the historical domestic production and international trade of a number of cobalt-containing commodities in the EU. Different data sources were examined for data, such as the British Geological Survey (BGS), the US Geological Survey (USGS), and the Eurostat and UN Comtrade (UNC) databases, considering all EU-member states before and after they joined the EU. For the international trade, hidden flows related to data gaps such as data reported in monetary value or recorded as "special category" were identified and included in the analysis. In addition, data from the Finnish customs database (ULJAS) was used to complement flows reported by Eurostat and UNC. From UNC, data was obtained considering the member states as reporters or as partners of the trade, due to internal differences of the database. Based on the acquired data the domestic production and international trade of the commodities were reconstructed for the timeframes 1938-2018 and 1988-2018, respectively. Next to the analysis of the trend of the production and trade of the different commodities, the importance of including hidden flows was revealed, where hidden flows represented more than 50% of the flow of a year in some cases. In addition, it was identified that even from reliable data sources, strong differences (more than 100% in some cases) can be found in the reported data, which is crucial to consider when utilizing the data in research.

| S-EPMC8291040 | biostudies-literature

Lessons from equilibrium statistical physics regarding the assembly of protein complexes.

Project description:Cellular functions are established through biological evolution, but are constrained by the laws of physics. For instance, the physics of protein folding limits the lengths of cellular polypeptide chains. Consequently, many cellular functions are carried out not by long, isolated proteins, but rather by multiprotein complexes. Protein complexes themselves do not escape physical constraints, one of the most important being the difficulty of assembling reliably in the presence of cellular noise. In order to lay the foundation for a theory of reliable protein complex assembly, we study here an equilibrium thermodynamic model of self-assembly that exhibits 4 distinct assembly behaviors: diluted protein solution, liquid mixture, "chimeric assembly," and "multifarious assembly." In the latter regime, different protein complexes can coexist without forming erroneous chimeric structures. We show that 2 conditions have to be fulfilled to attain this regime: 1) The composition of the complexes needs to be sufficiently heterogeneous, and 2) the use of the set of components by the complexes has to be sparse. Our analysis of publicly available databases of protein complexes indicates that cellular protein systems might have indeed evolved so as to satisfy both of these conditions.

| S-EPMC6955335 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data