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Blind system identification of two-thermocouple sensor based on cross-relation method.

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This study introduces a novel two-thermocouple method to eliminate dynamic errors in temperature measurements. The particle swarm optimization algorithm accurately estimates thermocouple time constants, improving dynamic temperature reconstruction.

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Area of Science:

  • Thermocouple thermometry
  • Dynamic temperature measurement
  • System identification

Background:

  • Dynamic sensor characteristics significantly impact temperature measurement accuracy.
  • Thermocouples, despite their advantages in harsh conditions, suffer from dynamic errors due to thermal inertia.
  • Accurate dynamic temperature measurement is crucial in various industrial and scientific applications.

Purpose of the Study:

  • To develop and validate a method for eliminating dynamic errors in thermocouple-based temperature measurements.
  • To accurately identify the dynamic characteristics of a two-thermocouple sensor system.
  • To reconstruct dynamic gas temperatures in constant velocity flow environments.

Main Methods:

  • Utilized a two-thermocouple sensor configuration for dynamic temperature measurement.
  • Applied blind system identification using a cross-relation method.
  • Employed the particle swarm optimization algorithm to estimate thermocouple time constants, comparing it with grid-based search.
  • Validated the method using experimental equipment with a high-temperature furnace.

Main Results:

  • The particle swarm optimization algorithm effectively estimated the time constants of the two thermocouples.
  • The developed method successfully reconstructed the input dynamic temperature using output data from the thermocouple with a smaller time constant.
  • The cross-relation based system identification provided accurate dynamic characteristics of the sensor.

Conclusions:

  • The two-thermocouple sensor and particle swarm optimization method effectively eliminate dynamic errors in temperature measurements.
  • This approach enables accurate reconstruction of dynamic gas temperatures, even in challenging environments.
  • The findings contribute to improved accuracy in dynamic temperature sensing applications.