A study of fluid temperature inversion based on multi-strategy cooperative improvement of CPO algorithm

•The inverse heat conduction problem is solved by a heuristic algorithm (MCI-CPO).•Compared to the original algorithm, MCI-CPO incorporates multiple strategies.•The performance improvement of the improved algorithm was tested.•The method significantly improves the inversion accuracy. During reactor...

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Vydáno v:Applied thermal engineering Ročník 267; s. 125864
Hlavní autoři: WANG, Shoubin, SUN, Wenhao, CHENG, Baohua, LI, Youbing, JING, Lewei, FANG, Xinchang, LV, Xuanman, SONG, Jie, ZHOU, Yuan, PENG, Guili
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.05.2025
Témata:
Inversion heat conduction problem
MCI-CPO
Surge line
Temperature detection
Thermal stratification
Inversion heat conduction problem
Surge line
Temperature detection
Thermal stratification
MCI-CPO
ISSN:1359-4311
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Shrnutí:•The inverse heat conduction problem is solved by a heuristic algorithm (MCI-CPO).•Compared to the original algorithm, MCI-CPO incorporates multiple strategies.•The performance improvement of the improved algorithm was tested.•The method significantly improves the inversion accuracy. During reactor start-up, shut-down, and normal operation, thermal stratification and temperature oscillations can occur within the surge line. These repeated temperature variations and associated stress effects may adversely affect the pipe material. For nuclear piping systems, it is essential to develop indirect, non-destructive methods to predict or evaluate the temperature distribution and variations along the interior walls of pipelines. This study presents a two-dimensional (2D) cross-sectional model of the pipe to analyze the thermal stratification problem in the surge line. An enhanced Crown Porcupine Optimization (CPO) algorithm is proposed to invert the measured outer wall surface temperature to the inner wall surface fluid temperature. The influence of the inversion heat conduction problem (IHCP) solving method on the calculation results is examined, and the multi-strategy collaborative improved Crown Porcupine Optimization algorithm (MCI-CPO) is compared with other algorithms. Experimental results demonstrate that the average absolute errors of the MCI-CPO, CPO, and Particle Swarm Optimization (PSO) algorithms are 0.04 ‰, 0.51‰, and 1.64 ‰, respectively. The maximum absolute errors are 0.1 ‰, 1.3 ‰, and 8.3 ‰, respectively. The theoretical temperature obtained by MCI-CPO inversion closely matches the measured temperature, significantly enhancing the accuracy of surge line thermal stratification analysis. Additionally, this approach provides a feasible solution for surge line temperature detection, offering valuable insights for nuclear pipeline engineering.
ISSN:1359-4311
DOI:10.1016/j.applthermaleng.2025.125864