Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

512
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...
512
Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

11.6K
Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...
11.6K
Typical Model Studies01:30

Typical Model Studies

678
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
678
Turbulent Flow01:24

Turbulent Flow

860
Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent...
860
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

602
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
602
Modeling and Similitude01:12

Modeling and Similitude

706
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
706

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smart strategies to navigate turbulent odor plumes reorienting to local wind.

ArXiv·2026
Same author

Policy heterogeneity improves collective olfactory search in three-dimensional turbulence.

Physical review. E·2026
Same author

TURB-Smoke. A database of Lagrangian pollutants emitted from point sources in turbulent flows with a mean wind.

Scientific data·2026
Same author

Assessment of Splenic Stiffness in a Cohort of Healthy Population Using 2-D Shear Wave Ultrasound Elastography.

Journal of clinical ultrasound : JCU·2026
Same author

Comparative transcriptome analysis reveals ABI3/VP1-WRKY25-STR1 regulatory module linking specialized metabolism with root system development and stress response in Rauvolfia serpentina.

BMC genomics·2026
Same author

Corpus Callosum Size on Magnetic Resonance Imaging and Its Association With Developmental Delays in Children: A Retrospective Case-Control Study.

Cureus·2026
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Mar 21, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.7K

Multiscale data assimilation in turbulent models.

Francesco Fossella1,2,3, Luca Biferale2,3, Alberto Carrassi4

  • 1Telecom Paris, LTCI, 19 Place Marguerite Perey, 91120 Palaiseau, France.

Physical Review. E
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

Data assimilation using the ensemble Kalman filter (EnKF) in turbulence models improves predictions. Measuring mesoscales synchronizes larger scales, offering a computationally efficient alternative to other data assimilation methods.

More Related Videos

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

13.1K
Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

11.3K

Related Experiment Videos

Last Updated: Mar 21, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.7K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

13.1K
Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

11.3K

Area of Science:

  • Fluid Dynamics
  • Computational Physics
  • Data Assimilation

Background:

  • Turbulence modeling involves complex multiscale dynamics.
  • Data assimilation (DA) is crucial for improving forecast accuracy.
  • Shell models offer a simplified framework for studying turbulence.

Purpose of the Study:

  • To investigate the impact of mesoscale measurements on turbulence prediction using data assimilation.
  • To evaluate the ensemble Kalman filter (EnKF) within a multiscale shell model.
  • To compare EnKF with nudging and ensemble four-dimensional variational methods.

Main Methods:

  • Utilized a shell model of turbulence.
  • Implemented the ensemble Kalman filter (EnKF) for data assimilation.
  • Systematically varied observation frequency and measured scales.
  • Benchmarked EnKF against nudging and 4D-Var.

Main Results:

  • Mesoscale measurements, exceeding observed scale frequencies, synchronize larger scales when at least two adjacent scales are measured.
  • EnKF demonstrated superior performance compared to nudging.
  • EnKF showed comparable results to 4D-Var with lower computational cost.
  • A scale-aware inflation technique is necessary for stable assimilation.

Conclusions:

  • EnKF is an effective and computationally efficient DA method for turbulent flows.
  • Targeted mesoscale observations significantly enhance predictions across scales.
  • Scale-aware inflation is critical for robust data assimilation in turbulence models.