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Free Jet01:14

Free Jet

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Free jets describe the flow of liquid exiting a reservoir through an opening into the atmosphere without resistance. The velocity (v) of the liquid jet is derived using Bernoulli's principle and expressed as:
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Irrotational flow is characterized by fluid motion where particles do not rotate around their axes, resulting in zero vorticity. For a flow to be irrotational, the curl of the velocity field must be zero. This imposes specific conditions on velocity gradients. For instance, to maintain zero rotation about the z-axis, the gradient condition:
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Bernoulli's Equation for Flow Normal to a Streamline01:16

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Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
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Coriolis Force

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An accelerating particle experiences a force equal to the mass multiplied by the acceleration in an inertial frame of reference. Consider a particle in a non-inertial frame of reference, such as a sliding ball on a rotating table. The acceleration of the ball in this rotating reference frame is different than in the intertial frame, which modifies its equation of motion. The fictitious forces acting additionally on a rotating frame of reference alter Newton's Second Law expression.
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Boundary Layer Characteristics

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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...
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Updated: Sep 15, 2025

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Explaining and predicting the Southern Hemisphere eddy-driven jet.

Julia Mindlin1, Theodore G Shepherd2,3, Marisol Osman4,5,6

  • 1Leipzig Institute for Meteorology, Leipzig 04103, Germany.

Proceedings of the National Academy of Sciences of the United States of America
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

Global warming and remote drivers explain the Southern Hemisphere eddy-driven jet (EDJ) shift. Climate models accurately project latitude changes but underestimate EDJ strengthening, highlighting the need for improved climate change predictions.

Keywords:
Southern Hemispherecausal networkseddy-driven jetnear-term projectionsstorylines

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Area of Science:

  • Atmospheric science
  • Climate dynamics
  • Southern Hemisphere meteorology

Background:

  • The Southern Hemisphere eddy-driven jet (EDJ) is crucial for regional weather patterns and climate.
  • Global climate models (GCMs) face challenges in accurately projecting EDJ trends due to uncertainties in remote drivers like tropical warming and stratospheric changes.
  • Predicting future EDJ behavior is vital for understanding regional climate impacts.

Purpose of the Study:

  • To develop a causal framework for attributing past EDJ changes.
  • To predict plausible future trajectories of the EDJ.
  • To bridge the gap between climate change attribution and prediction.

Main Methods:

  • Combined observational data, reanalysis datasets, and Coupled Model Intercomparison Project (CMIP) projections.
  • Developed a causal inference framework integrated with climate storylines.
  • Analyzed the influence of tropical warming and stratospheric polar vortex strengthening on EDJ trends.

Main Results:

  • Tropical warming is progressing at the lower end of CMIP projections.
  • The stratospheric polar vortex shows significant strengthening, impacting observed EDJ trends.
  • Global warming accounts for 50% of the observed EDJ latitude shift; remote drivers account for the other 50% with uncertain GW attribution.
  • GCMs accurately capture observed latitudinal shifts but underestimate EDJ strengthening.

Conclusions:

  • The developed causal framework effectively captures observed EDJ trends, outperforming standard GCMs in some aspects.
  • Integrating causal inference with climate storylines provides a physically grounded method for predicting future climate change pathways.
  • This approach enhances our ability to make more reliable climate change predictions and understand regional climate impacts.