Enabling High-Degree-of-Freedom Thermal Engineering Calculations via Lightweight Machine Learning

Yajing Tian (), Yuyang Wang, Shasha Yin, Jia Lu and Yu Hu
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Yajing Tian: State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China
Yuyang Wang: State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China
Shasha Yin: State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China
Jia Lu: State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China
Yu Hu: State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China

Energies, 2024, vol. 17, issue 16, 1-18

Abstract: U-tube steam generators (UTSGs) are crucial in nuclear power plants, serving as the interface between the primary and secondary coolant loops. UTSGs ensure efficient heat exchange, operational stability, and safety, directly impacting the plant’s efficiency and reliability. Existing UTSG models have fixed structures, which can only be used when certain parameters are given as model input. Such constraints hinder their ability to accommodate the diverse operating conditions, where input and output parameters can vary significantly. To address this challenge, we propose a machine learning-based method for developing a high-degree-of-freedom UTSG thermal model. The most notable feature of this approach is its capacity to flexibly interchange input and output parameters. By adopting comprehensive parameter sensitivity analysis, the most efficient method for training dataset generation is determined. Leveraging a lightweight machine learning method, the prediction accuracy for all UTSG parameters is improved to within 2.1%. The flexibility of the proposed machine learning approach ensures that the UTSG model can accommodate any type of parameter input without extensive reconfiguration of the model structure, thereby enhancing its applicability and robustness in real-world applications.

Keywords: steam generators; thermal model; sensitivity analysis; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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