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相关概念视频

Rapidly Varying Flow01:24

Rapidly Varying Flow

60
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
60
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

63
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...
63
Gradually Varying Flow01:29

Gradually Varying Flow

44
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
44
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

59
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
59
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

70
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...
70
Underflow Gates01:30

Underflow Gates

48
Underflow gates are vital for controlling water flow in irrigation canals. The three main types of underflow gates — vertical, radial, and drum gates — serve different purposes while ensuring effective flow management. Vertical gates move up and down, generating a free-flowing water jet; radial gates pivot to regulate the flow; and drum gates rotate for precise adjustments. The flow through these gates is influenced by downstream conditions, resulting in free or drowned outflow.Free and...
48

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相关实验视频

Updated: Jun 26, 2025

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

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可解释的基流细分和基于数值实验和深度学习的预测.

Qiying Yu1, Chen Shi2, Yungang Bai3

  • 1School of Water Conservancy and Transportation, Zhengzhou University, Henan, China; Xinjiang Institute of Water Resources and Hydropower Research, Xinjiang, 830049, China.

Journal of environmental management
|May 11, 2024
PubMed
概括

本研究介绍了一种新的方法,它结合了灰狼优化器数字过方法 (GWO-DFM) 和长短期记忆 (LSTM) 来分析高冷山区的基流. 这些发现揭示了影响基流的关键气候和土地因素,这对水资源管理至关重要.

关键词:
基准流量预测的预测基流分离器的基础流灰狼优化数字过器数字过器草是一个草.这是一个LSTM-SHAPAP.

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相关实验视频

Last Updated: Jun 26, 2025

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科学领域:

  • 水文和水资源管理 水文和水资源管理
  • 环境科学 环境科学
  • 环境建模中的人工智能

背景情况:

  • 基流对于高冷山区的下水流稳定至关重要,但由于数据稀缺和复杂的影响因素,其变异性不明.
  • 在这些数据稀缺的环境中,选择适当的基流分离方法和评估气候/土地表面影响是重大挑战.

研究的目的:

  • 开发和应用一种强大的基流分离和预测方法,用于高冷的山区.
  • 调查气象因素和潜在的表面变化对基流变化和季节性分布的影响.
  • 澄清基础流预测中长短期记忆 (LSTM) 模型的可解释性.

主要方法:

  • 利用灰狼优化器数字过方法 (GWO-DFM) 进行快速和最佳的基流分离,平均确定三种过方法为优越的.
  • 采用长短期记忆 (LSTM) 神经网络模型进行基流预测,达到超过0.78.7的纳什-萨克利夫效率系数.
  • 综合63年流量数据,气象数据 (ERA5-陆地) 和MODIS数据 (NDVI) 进行全面分析.

主要成果:

  • GWO-DFM有效地确定了最佳的过参数,查普曼,查普曼-马克斯韦尔和埃克哈特过器的算术平均值被证明是最合适的.
  • 底流来源主要与降水透,冰川结土壤和季节性池有关.
  • 太阳辐射,温度,降水和NDVI被确定为影响基流变化的主要因素,太阳辐射,温度和NDVI显示出最重要的影响.

结论:

  • 综合的GWO-DFM和LSTM方法为数据稀缺的高冷山区的基流分析提供了可靠的框架.
  • 了解气候和土地表面因素的复杂相互作用对于预测基流动力学至关重要.
  • 这项研究为面对环境变化的山区盆地可持续水资源管理提供了宝贵的见解.