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相关概念视频

Major Losses in Pipes01:28

Major Losses in Pipes

1.3K
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to...
1.3K
Minor Losses in Pipes01:25

Minor Losses in Pipes

1.2K
In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
1.2K
Energy Losses in Transformers01:21

Energy Losses in Transformers

985
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
985
Line Loss01:10

Line Loss

305
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
305
Reducing Line Loss01:18

Reducing Line Loss

197
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
197
Design Example: Designing a Residential Plumbing System01:25

Design Example: Designing a Residential Plumbing System

793
The design of residential plumbing systems requires carefully evaluating water demand, flow rates, and pressure dynamics to ensure both efficiency and reliability. The nature of water flow within pipes is defined by its Reynolds number, which classifies flow as either laminar (smooth) or turbulent.
793

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相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

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工业4.0中的管道阻力损失计算:基于TransKAN和生成AI的创新框架

Qinyu Zhang1, Huiying Liu1, Zhike Liu2

  • 1College of Science, North China University of Science and Technology, Tangshan 063210, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括

本研究介绍了一种AI框架,用于准确预测回填采矿中的管道阻力损失,这对于减少能源消耗和提高效率至关重要. 与传统方法相比,新型的TransKAN模型提供了更高的准确性.

关键词:
在KAN网络中,KAN是KAN网络.聚合注意力 聚合注意力生成型的人工智能 (GAI)管道阻力损失的情况

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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

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相关实验视频

Last Updated: Sep 18, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

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2.1K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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Surrogate Model Development for Digital Experiments in Welding
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Surrogate Model Development for Digital Experiments in Welding

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科学领域:

  • 采矿工程 采矿工程 采矿工程
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 在回填采矿中优化管道运输对于深度矿产开采效率至关重要.
  • 由于矿井的变化,精确计算管道阻力损失是具有挑战性的,影响工艺效率.
  • 工业4.0为采矿业务的智能优化提供数据驱动的解决方案.

研究的目的:

  • 开发一个新的管道阻力损失预测框架,用于回填采矿.
  • 提高阻力损失计算的准确性,从而减少能量损失和改善填充效果.
  • 为此目的,利用生成性人工智能和先进的神经网络架构.

主要方法:

  • 整合生成型人工智能,用于创建物理约束的增强数据.
  • 使用KAN (科尔摩戈罗夫-阿诺德网络) 模型与B-spline基础函数用于非线性特征提取.
  • 应用变压器架构来捕捉管道压力数据中的时空相关性.

主要成果:

  • 拟议的TransKAN模型实现了0.9644.4的高R平方值.
  • 该模型表现出卓越的性能,RMSE为0.7126和MAE为0.4703.
  • 经验验证使用管道压力传感器的实验数据证实了模型的准确性.

结论:

  • 这种由人工智能驱动的新型框架提供了一种精确的方法来计算回填采矿中的管道阻力损失.
  • TransKAN模型显著优于传统方法和现有的机器学习模型.
  • 这一进步支持在深度采矿中智能优化能源消耗和运营效率.