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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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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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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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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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Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements.

Adrián García-Gutiérrez1, Diego Domínguez1, Deibi López1

  • 1Aerospace Engineering Area, Universidad de León, 24071 León, Spain.

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Summary

This study presents a novel neural network method to estimate wind profiles in the Atmospheric Boundary Layer (ABL) using only near-ground data. This approach offers improved accuracy and temporal analysis for wind resource assessment and CFD models.

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Area of Science:

  • Atmospheric Science
  • Meteorology
  • Renewable Energy

Background:

  • Accurate wind profile estimation is crucial for wind energy applications.
  • Traditional methods often require extensive measurements or complex models.
  • Near-ground measurements are readily available but limited in scope.

Purpose of the Study:

  • To develop and validate a new methodology for estimating wind profiles in the Atmospheric Boundary Layer (ABL).
  • To utilize a neural network trained on lidar data for wind profile prognosis.
  • To enable temporal evolution studies of wind profiles.

Main Methods:

  • A neural network (multilayer perceptron) was trained using lidar-collected data.
  • Sensibility analyses were performed to determine the optimal network configuration.
  • The network uses near-ground measurements as input for wind profile estimation.

Main Results:

  • The trained neural network accurately estimates wind profiles (u and v components) in the ABL.
  • The proposed method demonstrates superior performance compared to traditional techniques.
  • The system requires only near-surface measurements post-training.

Conclusions:

  • The developed neural network methodology provides an efficient and accurate way to estimate wind profiles.
  • This technique simplifies wind resource assessment and enhances computational fluid dynamics (CFD) modeling.
  • The method's ability to study temporal wind profile evolution is a significant advancement.