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Related Concept Videos

Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

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Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
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Pressure Variation in a Fluid at Rest01:11

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In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
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Linear Approximation in Time Domain01:21

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Static, Stagnation, Dynamic and Total Pressure01:24

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The concept of static, stagnation, dynamic, and total pressure is fundamental in fluid dynamics, often explained using Bernoulli's equation:
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Distributed Sensor Network Calibration Under Sensor Nonlinearities with Applications in Aerodynamic Pressure Sensing.

Srdjan S Stanković1, Miloš S Stanković2,3, Mladen Veinović2

  • 1School of Electrical Engineering, University of Belgrade, 11020 Belgrade, Serbia.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces blind calibration algorithms for sensor networks, improving accuracy without prior signal knowledge. A modified consensus algorithm enhances robustness against nonlinearities and noise for real-time recalibration.

Keywords:
aerodynamic pressure sensingblind macro-calibrationbounded input–bounded output stabilitydynamic consensus schemessensor networkssensor nonlinearities

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Networked Systems

Background:

  • Large sensor networks require accurate calibration for reliable data.
  • Blind calibration methods estimate sensor parameters without external references.
  • Existing consensus-based algorithms face challenges with nonlinearities and noise.

Purpose of the Study:

  • To develop and analyze distributed blind calibration algorithms for large sensor networks.
  • To rigorously assess algorithm performance under nonlinear sensor models and noisy environments.
  • To provide practical recommendations for real-time sensor network recalibration.

Main Methods:

  • Development of consensus-based distributed blind calibration algorithms.
  • Analysis using stochastic approximation theory for nonlinear models and stochastic environments.
  • Stability analysis in the bounded input-bounded output sense.
  • Simulation studies and experimental verification using a multichannel aerodynamic pressure sensing instrument.

Main Results:

  • A modified blind calibration algorithm demonstrates superior robustness to sensor nonlinearities compared to a standard consensus approach.
  • Theoretical stability proofs are provided for the algorithms under bounded input-bounded output conditions.
  • Simulation results comprehensively illustrate algorithm properties relevant to practical applications.
  • Experimental validation confirms the algorithm's effectiveness in real-time recalibration of a complex sensor system.

Conclusions:

  • Distributed blind calibration algorithms based on consensus are effective for sensor networks.
  • The modified algorithm offers enhanced robustness and practical utility for online recalibration.
  • The developed methods provide a simple and efficient tool for maintaining sensor network accuracy during operation.