Real-Time Sensor for Measuring the Surface Temperature of Thermal Protection Structures Based on the Full-Time Domain Temperature Inversion Method

The real-time surface thermal monitoring of thermal protection structures (TPSs) is crucial for hypersonic vehicle safety. This study proposes an effective approach for real-time temperature reconstruction by integrating embedded sensor arrays with an enhanced full-time domain inversion algorithm, utilizing the overlapping sliding window method. An array of three evenly spaced sensors is used for TPS monitoring. Notably, the inversion approach eliminates the need for prior knowledge of the TPS's thermal parameters. It exhibits remarkable practicality with low-frequency sampling requirements (1 Hz) and robust noise resistance. Through numerical simulations and a quartz lamp side heating experiment, it is demonstrated that the window size and data noise have great influence on the temperature reconstruction accuracy, but the window slip step has little influence. The mean relative error of the inversion temperature decreases exponentially as the window size increases, and the optimal window duration is equal to the thermal hysteresis time. The study investigates the impact of three noise filtering methods on the inversion accuracy, finding that the Savitzky-Golay filtering significantly enhances measurement precision, reducing mean relative error from 18.4% to 6.7%. These results highlight the potential of the proposed real-time sensor method for practical engineering applications, offering a reliable and efficient solution for real-time TPS temperature monitoring.

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Publication Details

Journal
Sensors
Published
2025-04-02
DOI
https://doi.org/10.3390/s25072227
Citations
2
Primary Topic
Advanced Sensor Technologies Research
Type
article
Field-Weighted Citation Impact
1.35

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article

Real-Time Sensor for Measuring the Surface Temperature of Thermal Protection Structures Based on the Full-Time Domain Temperature Inversion Method

Kok‐Sing Lim, Pengyu Nan, Hangzhou Yang, X. F. Zhao et al.
2 citations
Sensors
Advanced Sensor Technologies Research
1.35
article

Real-Time Sensor for Measuring the Surface Temperature of Thermal Protection Structures Based on the Full-Time Domain Temperature Inversion Method

Kok‐Sing Lim, Pengyu Nan, Hangzhou Yang, X. F. Zhao, Yuhao Liu, Fan Zhou, Yupeng Zhang, Guoguo Xin, Xiangyu Wei
article en
2 citations

Abstract

The real-time surface thermal monitoring of thermal protection structures (TPSs) is crucial for hypersonic vehicle safety. This study proposes an effective approach for real-time temperature reconstruction by integrating embedded sensor arrays with an enhanced full-time domain inversion algorithm, utilizing the overlapping sliding window method. An array of three evenly spaced sensors is used for TPS monitoring. Notably, the inversion approach eliminates the need for prior knowledge of the TPS's thermal parameters. It exhibits remarkable practicality with low-frequency sampling requirements (1 Hz) and robust noise resistance. Through numerical simulations and a quartz lamp side heating experiment, it is demonstrated that the window size and data noise have great influence on the temperature reconstruction accuracy, but the window slip step has little influence. The mean relative error of the inversion temperature decreases exponentially as the window size increases, and the optimal window duration is equal to the thermal hysteresis time. The study investigates the impact of three noise filtering methods on the inversion accuracy, finding that the Savitzky-Golay filtering significantly enhances measurement precision, reducing mean relative error from 18.4% to 6.7%. These results highlight the potential of the proposed real-time sensor method for practical engineering applications, offering a reliable and efficient solution for real-time TPS temperature monitoring.

SensorsVol. 25(7)
University of Malaya (MY), Northwest University (CN), Hefei Institute of Technology Innovation (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 21%
Advanced Sensor Technologies Research
1.35
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