Yajing Tian, Yuyang Wang, Shasha Yin, Jia Lu, Yu Hu
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引用次数: 0
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.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
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CAS
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