Infrared and Laser Engineering, Volume. 52, Issue 11, 20230103(2023)

A fast method for predicting transient temperature field of ground target based on limited measuring point data

Ying Chen1,2, Dengfeng Ren1,2, and Yuge Han1,2
Author Affiliations
  • 1School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • 2MIIT Key Laboratory of Thermal Control of Electronic Equipment, Nanjing University of Science and Technology, Nanjing 210094, China
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    Figures & Tables(23)
    Diagram of POD implementation process
    Data processing flow diagram (square cavity temperature field)
    Schematic diagram of square cavity test device
    (a) Diagram of square cavity with background temperature;(b) Diagram of square cavity removed background temperature
    Air temperature variation curve from July 7th to 9th
    (a) Energy contribution rate of the first 20 POD modes of square cavity;(b) Cumulative energy contribution rate of the first 20 POD modes of square cavity
    (a) Original temperature field of square cavity at all times;(b) Temperature field of square cavity predicted by 16 POD modes;(c) Temperature field of square cavity predicted by 30 POD modes
    Comparison of temperature field prediction errors with different sensors
    (a) Square cavity sensor distribution at 12:00 on July 7th; (b) Temperature field of square cavity predicted by POD mode; (c) Temperature field of square cavity predicted by sensors
    (a) Square cavity sensor distribution at 12:00 on July 8th; (b) Temperature field of square cavity predicted by POD mode; (c) Temperature field of square cavity predicted by sensors
    (a) Schematic diagram of typical positions on the surface of square cavity;(b) Typical position 1;(c) Typical position 2;(d) Typical position 3
    (a) Diagram of the tank geometry model with heat producer; (b) Schematic diagram of the tank grid with heat producer
    Air temperature variation curve from April 8th to 10th
    (a) Energy contribution rate of the first 20 POD modes of 24 trains of tank model;(b) Cumulative energy contribution rate of the first 20 POD modes of 24 trains of tank model;(c) Energy contribution rate of the first 20 POD modes of 48 trains of tank model;(d) Cumulative energy contribution rate of the first 20 POD modes of 48 trains of tank model
    (a) Original temperature field of tank at all times;(b) Temperature field of 24 trains of tank predicted by 20 POD modes;(c) Temperature field of 48 trains of tank predicted by 20 POD modes
    (a) Sensor temperature field distribution of 24 trains of tank at 13:00 on April 10th;(b) Temperature field predicted result by 24 trains of tank;(c) Sensor temperature field distribution of 48 trains of tank at 13:00 on April 10th;(d) Temperature field predicted result by 48 trains of tank
    (a) Sensor temperature field distribution of 24 trains of tank at 20:00 on April 10th;(b) Temperature field predicted result by 24 trains of tank;(c) Sensor temperature field distribution of 48 trains of tank at 20:00 on April 10th;(d) Temperature field predicted result by 48 trains of tank
    (a) Schematic diagram of typical positions on the surface of tank;(b) Typical position 1;(c) Typical position 2
    • Table 1. Temperature field prediction error with different POD modes

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      Table 1. Temperature field prediction error with different POD modes

      TimeError of 16 POD modes/K Error of 30 POD modes/K
      12 o 'clock on July 7th1.0531.047
      12 o 'clock on July 8th1.0631.051
      12 o 'clock on July 8th1.0591.05
    • Table 2. Temperature field prediction error with different sensors

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      Table 2. Temperature field prediction error with different sensors

      TimeError of 16 POD modes/KError of 16 sensors/KError of 30 POD modes/KError of 30 sensors/K
      12 o 'clock on July 7th1.053 1.102 1.047 1.09
      12 o 'clock on July 8th1.062 1.318 1.051 1.09
    • Table 3. Temperature field prediction error with 20 POD modes of two reduced order models

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      Table 3. Temperature field prediction error with 20 POD modes of two reduced order models

      TimeError of 24 trains/KError of 48 trains/K
      10 o 'clock on April 10th2.29841.9195
      13 o 'clock on April 10th1.86251.8451
      20 o 'clock on April 10th1.79611.8004
    • Table 4. Temperature field prediction error with 30 sensors of two reduced order models

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      Table 4. Temperature field prediction error with 30 sensors of two reduced order models

      TimeError of 24 trains/KError of 48 trains/K
      13 o 'clock on April 10th2.16342.1981
      20 o 'clock on April 10th2.131.9872
    • Table 5. Running time of different algorithms

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      Table 5. Running time of different algorithms

      AlgorithmsRunning time/s
      Theoretical modeling600
      POD8.5316
      POD combined with QR5.3495
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    Ying Chen, Dengfeng Ren, Yuge Han. A fast method for predicting transient temperature field of ground target based on limited measuring point data[J]. Infrared and Laser Engineering, 2023, 52(11): 20230103

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    Paper Information

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    Received: Feb. 28, 2023

    Accepted: --

    Published Online: Jan. 8, 2024

    The Author Email:

    DOI:10.3788/IRLA20230103

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