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

Dimensional Analysis01:23

Dimensional Analysis

910
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
910
Boundary Layer Characteristics01:18

Boundary Layer Characteristics

175
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
175
Correlation of Experimental Data01:23

Correlation of Experimental Data

251
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
251
Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

140
During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance.
140
Dimensionless Groups in Fluid Mechanics01:15

Dimensionless Groups in Fluid Mechanics

361
Dimensionless groups in fluid mechanics provide simplified ratios that help analyze fluid behavior without relying on specific units. The Reynolds number (Re), which represents the ratio of inertial to viscous forces, distinguishes between laminar and turbulent flows, making it essential in the design of pipelines and aerodynamic surfaces. The Froude number (Fr), the ratio of inertial to gravitational forces, is particularly useful in predicting wave formation and hydraulic jumps in...
361
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K

You might also read

Related Articles

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

Sort by
Same author

Leveraging deep learning-based foundation models for optical turbulence (<i>C</i><i>n</i>2) estimation under data scarcity.

Applied optics·2026
Same author

Single shot line-of-sight atmospheric turbulence profiling for laser satellite communications with STORM.

Applied optics·2026
Same author

Seidel optical aberrations and optimum truncated Gaussian beams on intersatellite free-space optical communications.

Optics express·2025
Same author

Analysis of atmospheric turbulence dynamics during the total solar eclipse with AI-based sensing.

Journal of the Optical Society of America. A, Optics, image science, and vision·2025
Same author

Reducing beam tracking complexity using a phase ramp and Fresnel lens when steering beams using spatial light modulators.

Optics letters·2024
Same author

Intercomparison of flux-, gradient-, and variance-based optical turbulence (<i>C</i> <i>n</i>2) parameterizations.

Applied optics·2024

Related Experiment Video

Updated: Jul 17, 2025

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

12.3K

Π-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface

Maximilian Pierzyna, Rudolf Saathof, Sukanta Basu

    Optics Letters
    |September 1, 2023
    PubMed
    Summary

    We developed a physics-informed machine learning model to estimate optical turbulence strength (Cn2) for free-space optical communications. The model accurately predicts Cn2 using normalized variance of potential temperature as a key feature.

    More Related Videos

    Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
    06:48

    Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

    Published on: May 10, 2020

    3.6K
    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

    7.1K

    Related Experiment Videos

    Last Updated: Jul 17, 2025

    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

    12.3K
    Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
    06:48

    Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

    Published on: May 10, 2020

    3.6K
    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

    7.1K

    Area of Science:

    • Physics
    • Atmospheric Science
    • Optical Engineering

    Background:

    • Optical turbulence, caused by atmospheric refractive index fluctuations, significantly distorts laser beams.
    • Accurate modeling of optical turbulence strength (Cn2) is crucial for reliable free-space optical (FSO) communication systems.

    Purpose of the Study:

    • To propose a novel physics-informed machine learning (ML) methodology, named Π-ML, for estimating Cn2.
    • To identify key atmospheric parameters influencing Cn2 through feature importance analysis.

    Main Methods:

    • Utilized dimensional analysis and gradient boosting for the ML model.
    • Employed an ensemble of models for enhanced statistical robustness.
    • Conducted systematic feature importance analysis to identify predictive drivers of Cn2.

    Main Results:

    • Identified normalized variance of potential temperature as the most dominant feature for predicting Cn2.
    • Achieved high out-of-sample performance with an R² value of 0.958 ± 0.001.
    • Demonstrated the effectiveness of the Π-ML approach in modeling optical turbulence.

    Conclusions:

    • The proposed Π-ML methodology provides an accurate and robust method for estimating optical turbulence strength.
    • This advancement is vital for the successful development and deployment of future FSO communication links.
    • Highlighting the importance of potential temperature variance offers new insights into atmospheric turbulence modeling.