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Related Concept Videos

Major Losses in Pipes01:28

Major Losses in Pipes

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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...
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Minor Losses in Pipes01:25

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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.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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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.
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The first cause can be  the high resistance of the...
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Line Loss01:10

Line Loss

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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...
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Reducing Line Loss01:18

Reducing Line Loss

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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...
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Design Example: Designing a Residential Plumbing System01:25

Design Example: Designing a Residential Plumbing System

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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.
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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI.

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
Summary
This summary is machine-generated.

This study introduces an AI framework to accurately predict pipeline resistance loss in backfill mining, crucial for reducing energy consumption and improving efficiency. The novel TransKAN model offers superior accuracy compared to traditional methods.

Keywords:
KAN networkattention fusiongenerative artificial intelligencepipeline resistance loss

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Optimizing pipeline transportation in backfill mining is critical for deep mineral extraction efficiency.
  • Accurate calculation of pipeline resistance loss is challenging due to mine variations, impacting process efficiency.
  • Industry 4.0 offers data-driven solutions for intelligent optimization in mining operations.

Purpose of the Study:

  • To develop a novel pipeline resistance loss prediction framework for backfill mining.
  • To enhance the accuracy of resistance loss calculations, thereby reducing energy loss and improving filling effects.
  • To leverage generative artificial intelligence and advanced neural network architectures for this purpose.

Main Methods:

  • Integration of generative artificial intelligence for creating physically constrained augmented data.
  • Utilization of the KAN (Kolmogorov-Arnold Network) model with B-spline basis functions for nonlinear feature extraction.
  • Application of the Transformer architecture to capture spatio-temporal correlations in pipeline pressure data.

Main Results:

  • The proposed TransKAN model achieved a high R-squared value of 0.9644.
  • The model demonstrated superior performance with an RMSE of 0.7126 and an MAE of 0.4703.
  • Empirical validation using experimental data from pipeline pressure sensors confirmed the model's accuracy.

Conclusions:

  • The novel AI-driven framework provides a precise method for calculating pipeline resistance loss in backfill mining.
  • The TransKAN model significantly outperforms traditional methods and existing machine learning models.
  • This advancement supports intelligent optimization of energy consumption and operational efficiency in deep mining.