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相关概念视频

Rapidly Varying Flow01:24

Rapidly Varying Flow

57
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...
57
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

308
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
308
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

98
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
98
Gradually Varying Flow01:29

Gradually Varying Flow

41
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...
41
Signal Flow Graphs01:18

Signal Flow Graphs

205
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
205
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

60
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...
60

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相关实验视频

Updated: Jun 19, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.8K

使用复杂网络进行流量预测.

Abdul Wajed Farhat1, B Deepthi1, Bellie Sivakumar1

  • 1Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
概括

这项研究引入了一种新的复杂网络方法,用于流量预测. 该方法使用流量数据来构建网络并预测未来的流量,在整个连接的美国显示出有希望的结果.

科学领域:

  • 水文与水资源工程 水文与水资源工程
  • 复杂系统分析 复杂系统分析
  • 时间序列预测时间序列预测

背景情况:

  • 可靠的流量预测对于水资源管理,环境监测和生态系统健康至关重要.
  • 现有的流量预测方法在捕捉复杂的时间动态方面经常面临挑战.
  • 复杂网络为分析和建模时间序列数据提供了一个新的框架.

研究的目的:

  • 开发和评估一种复杂的基于网络的方法,用于日常流量预测.
  • 用已确定的统计指标来评估预测准确度.
  • 调查预测准确度和采集区特征之间的关系.

主要方法:

  • 流程时间序列被转化为复杂的网络,时间步骤是节点,流程差异定义链接.
  • 确定了一个关键距离值,以形成网络结构.
  • 集群系数 (CC) 被计算并用于对最近邻居进行预测的搜索.

主要成果:

  • 复杂网络方法应用于连续美国的142个站点,产生0.05到0.99.99的相关系数 (R) 的预测准确度.
  • 正常化根平均平方误差 (NRMSE) 范围从0.1到12.3,Nash-Sutcliffe效率 (NSE) 范围从-0.89到0.99.
  • 预测准确度与流量变化系数的关系比与排水面积或平均流量关系更强.
关键词:
聚类系数的聚类系数变化系数的变化系数连续的美国 连续的美国距离门的距离门.最接近邻居的方法.网络理论 网络理论

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Last Updated: Jun 19, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

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结论:

  • 提出的基于复杂网络的方法为准确的流量预测提供了一个有前途的工具.
  • 该方法在美国的各种水文条件中显示出有效的应用.
  • 进一步的研究可以将这种方法扩展到其他水文时间序列预测应用.