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Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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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...
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Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

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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.
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Boundary Layer Characteristics01:18

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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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Related Experiment Video

Updated: Aug 31, 2025

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Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.

Yuchao Zhu1, Rong-Hua Zhang1, James N Moum2

  • 1CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China.

National Science Review
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

A new deep-learning model improves ocean mixing parameterizations, reducing climate modeling biases. This data-driven approach enhances tropical Pacific simulations, offering a novel path for more accurate climate predictions.

Keywords:
artificial neural networks under physics constraintclimate model biaseslong-term turbulence dataocean vertical-mixing parameterizationsphysics-informed deep learning

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Area of Science:

  • Oceanography
  • Climate Science
  • Machine Learning

Background:

  • Ocean mixing parameterizations are crucial for climate models but often inaccurate in the tropics due to limited process understanding.
  • Traditional physics-driven models struggle with tropical oceanic vertical mixing.
  • Advances in deep learning and turbulence measurements enable data-driven approaches.

Purpose of the Study:

  • To develop and evaluate a novel, data-driven parameterization for oceanic vertical mixing using artificial neural networks.
  • To improve the accuracy of ocean and climate modeling, particularly in tropical regions.

Main Methods:

  • Developed an artificial neural network (ANN) parameterization for oceanic vertical mixing.
  • Trained the ANN using a decade of hydrographic and turbulence observations from the tropical Pacific.
  • Integrated the data-driven parameterization into an ocean model for testing.

Main Results:

  • The novel ANN parameterization demonstrated higher accuracy compared to existing methods.
  • The parameterization showed good generalization capabilities within physical constraints.
  • Integration into ocean models led to improved simulations in both ocean-only and coupled scenarios.

Conclusions:

  • Data-driven, physics-informed deep learning offers a feasible approach for enhancing oceanic vertical mixing parameterizations.
  • This method can significantly reduce biases in ocean and climate modeling.
  • The study highlights the potential of machine learning for geophysical fluid dynamics and improved climate simulations.