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Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning
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Yijia ZHANG, Shaojun REN, Baoyu ZHU, Qihang WENG, Zihan WEI, Fengqi SI
Thermal Power Generation | 2025, 54(11) : 107 - 116
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Thermal Power Generation | 2025, 54(11): 107-116
Thermal energy science research
Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning
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Yijia ZHANG, Shaojun REN, Baoyu ZHU, Qihang WENG, Zihan WEI, Fengqi SI
Affiliations
  • Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China
Published: 2025-11-25 doi: 10.19666/j.rlfd.202501029
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The effectiveness of a data-driven model relies on the completeness of its training samples. For operating conditions beyond the scope of the training samples, the model’s generalization ability is compromised. Therefore, to develop a condenser model that can adapt to the wide load variation of the unit, it is essential for the training samples to involve a diverse range of power generation loads and ambient temperatures. However, achieving this complete dataset is difficult for newly-commissioned units because of their short operation time. To address these challenges, a method for characterizing condensing units using multi-fidelity data and transfer learning is proposed, even with incomplete data. In this method, a pre-trained model is firstly built based on the comprehensive operational dataset collected from a similar unit. On the basis of the pre-trained model, additional linear and nonlinear calibration networks are introduced. The calibration networks are updated through the incomplete data of newly constructed units, enabling the transfer of the pre-training model to the feature space that is adapted to the incomplete dataset. The effectiveness of this method is validated through the condenser of a 1 000 MW supercritical unit. The results indicate that, even with limited training samples, the method accurately predicts parameters such as condenser pressure and circulating water outlet temperature, with an average R2 of 0.95, significantly outperforming the conventional data-driven model based on a single data set, of which the average R2 is only 0.81.

transfer learning  /  multi-fidelity data  /  deep neural network  /  condenser  /  power plant
Yijia ZHANG, Shaojun REN, Baoyu ZHU, Qihang WENG, Zihan WEI, Fengqi SI. Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning[J]. Thermal Power Generation, 2025 , 54 (11) : 107 -116 . DOI: 10.19666/j.rlfd.202501029
  • National Natural Science Foundation of China(52306230)
  • National Key Research & Development Program(2022YFB4100700)
Year 2025 volume 54 Issue 11
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Article Info
doi: 10.19666/j.rlfd.202501029
  • Receive Date:2025-01-27
  • Online Date:2026-01-13
  • Published:2025-11-25
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  • Received:2025-01-27
Funding
National Natural Science Foundation of China(52306230)
National Key Research & Development Program(2022YFB4100700)
Affiliations
    Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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