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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Turbulent Flow: Problem Solving01:09

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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.
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Uniform Depth Channel Flow: Problem Solving01:18

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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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Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

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Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
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Laminar and Turbulent Flow01:07

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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...
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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
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Related Experiment Video

Updated: Jun 5, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.6K

Physics-guided deep learning for skillful wind-wave modeling.

Xinxin Wang1,2,3, Haoyu Jiang1,2,3

  • 1College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.

Science Advances
|December 4, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning wave model accurately predicts significant wave height using historical wind data. This AI approach is faster and more efficient than traditional numerical wave models for oceanographic research.

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Area of Science:

  • Oceanography
  • Computational Fluid Dynamics
  • Artificial Intelligence

Background:

  • Accurate sea surface wind-wave modeling is vital for oceanography and engineering.
  • Current numerical wave models, while accurate, are computationally intensive and have limitations in representing wave spectral evolution.

Purpose of the Study:

  • To develop a novel deep learning-based wave model for predicting significant wave height.
  • To overcome the computational expense and physical representation limitations of traditional numerical wave models.

Main Methods:

  • A deep learning model was trained using observation-merged wave hindcasts.
  • The model directly predicts significant wave height, bypassing complex wave spectral information.
  • Physics-informed feature engineering guided the model by considering local and remote wind influences.

Main Results:

  • The AI wave model achieved higher accuracy than state-of-the-art numerical models.
  • It can model 1 year of global significant wave heights at a 0.5° × 0.5° × 1-hour resolution in under 30 minutes on a personal computer.

Conclusions:

  • Deep learning offers a computationally efficient and accurate alternative for sea surface wind-wave modeling.
  • This AI approach significantly reduces the complexity and resource requirements for wave prediction.