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相关概念视频

Precipitation Processes01:12

Precipitation Processes

484
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
484
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

95
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...
95
Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

2.2K
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.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
2.2K
Boundary Layer Characteristics01:18

Boundary Layer Characteristics

172
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...
172
Static, Stagnation, Dynamic and Total Pressure01:24

Static, Stagnation, Dynamic and Total Pressure

424
The concept of static, stagnation, dynamic, and total pressure is fundamental in fluid dynamics, often explained using Bernoulli's equation:
424
Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

1.4K
A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity...
1.4K

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相关实验视频

Updated: Jul 16, 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

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基于巴罗热带原始方程的网络,用于预测风速.

Rui Ye1, Baoquan Zhang1, Xutao Li1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
|September 11, 2023
PubMed
概括

本研究介绍了物理方程预测网络 (PEPNet),通过将物理动态知识集成到深度学习模型中,改进了多步风速预测. 通过结合基于物理和数据的方法,PEPNet提高了预测的稳定性和准确性.

科学领域:

  • 气象学和大气科学 气象学和大气科学
  • 计算科学与工程 计算科学与工程
  • 人工智能和机器学习

背景情况:

  • 深度学习方法对风速预测有希望,但往往忽视了明确的气象动态,限制了长期稳定性.
  • 由于缺乏物理理解,现有的数据驱动方法在稳定和长期的风速预测方面扎.

研究的目的:

  • 提出一个新的深度学习框架,即物理方程预测网络 (PEPNet),用于增强的多步风速预测.
  • 将来自气象动态的明确物理知识集成到神经网络中,以实现更强大的预测.

主要方法:

  • 开发了Augmented Neural Barotropic Equations (ANBE) 块,结合了基于物理学的分支 (神经巴洛特罗普方程单元) 和数据驱动的分支.
  • 基于物理学的分支模型使用巴罗热带原始方程来模拟时间导数,而数据驱动的分支则捕捉了超出这个假设的动态.
  • 在PEPNet中采用时间变量结构,以动态适应不断变化的风力动态.

主要成果:

  • 与最先进的方法相比,PEPNet在多步风速预测任务中表现优异.
  • 在真实世界数据集上的实验结果验证了将物理知识集成到深度学习模型中的有效性.
  • 提出的方法实现了最佳的预测性能,表明稳定性和准确性得到了增强.
关键词:
神经网络的神经网络的神经网络巴罗热带原始方程模式.预测风速的预测

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结论:

  • 物理方程预测网络 (PEPNet) 通过结合明确的气象动态,有效地增强了多步风速预测.
  • 在神经网络中结合基于物理和数据的方法,可以实现更稳定,更准确的长期风速预测.
  • 在将深度学习应用于气象预报方面,PEPNet代表了显著的进步,其性能优于现有的技术.