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Research on prediction model for spiral water-cooled wall temperature based on machine learning
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Maobo YUAN1, Lei DENG1, Xuemin LIU2, Kaixuan YANG1, 3, Yong LIANG1, Hu LIU1, Yaodong DA2, Defu CHE1
Thermal Power Generation | 2023, 52(8) : 32 - 39
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Thermal Power Generation | 2023, 52(8): 32-39
Thermal energy science research
Research on prediction model for spiral water-cooled wall temperature based on machine learning
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Maobo YUAN1, Lei DENG1, Xuemin LIU2, Kaixuan YANG1, 3, Yong LIANG1, Hu LIU1, Yaodong DA2, Defu CHE1
Affiliations
  • 1.State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
  • 2.China Special Equipment Inspection and Research Institute, Beijing 100029, China
  • 3.Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China
Published: 2023-08-25 doi: 10.19666/j.rlfd.202212204
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In the study, a computational fluid dynamics (CFD) model based on a 600 MW tangentially coal-fired boiler was established. According to orthogonal conditions (L16(45)), the heat flux distributions of the water-cooled wall under 100% BMCR, 75% THA, 50% THA and 35% BMCR loads were obtained. In addition, the factors also included: primary to secondary air rate, degree of air-staging, swing angles of burners and SOFA nozzles. Then, the spiral water-cooled wall temperature distributions under various conditions were calculated through coupling the heat absorption, temperature calculation and hydrodynamic characteristics of the water-cooled wall. Due to the discontinuity of orthogonal condition, the machine learning was used for predicting the spiral water-cooled wall temperature distribution within the range of parameters covered by orthogonal conditions. The results showed that a wall temperature peak up to 730 K would appear in the area among burner system. The heat transfer deterioration was easy to occur when the flame center height in furnace coincided with the phase change height of the working fluid during the boiler load adjusting process. The goodness of fit R2 of the ensemble learning on the training set and the test set of the wall temperature data had reached 0.99, which could be used to predict the wall temperature of the boiler under wide load. At the same time, the machine learning established the mapping relationship between the wall temperature distribution and the operating parameters of the boiler. In the future study, the wall temperature safety of the water wall can be guaranteed by reasonably adjusting and optimizing the operating parameters through the optimization algorithm.

spiral water-cooled wall  /  heat flux distribution  /  temperature distribution of water-cooled wall  /  orthogonal condition  /  machine learning
Maobo YUAN, Lei DENG, Xuemin LIU, Kaixuan YANG, Yong LIANG, Hu LIU, Yaodong DA, Defu CHE. Research on prediction model for spiral water-cooled wall temperature based on machine learning[J]. Thermal Power Generation, 2023 , 52 (8) : 32 -39 . DOI: 10.19666/j.rlfd.202212204
  • National Key Research and Development Program(2017YFB0602102)
Year 2023 volume 52 Issue 8
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Article Info
doi: 10.19666/j.rlfd.202212204
  • Online Date:2026-01-26
  • Published:2023-08-25
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History
  • Revised:2022-12-26
Funding
National Key Research and Development Program(2017YFB0602102)
Affiliations
    1.State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
    2.China Special Equipment Inspection and Research Institute, Beijing 100029, China
    3.Shanghai Power Equipment Research Institute Co., Ltd., Shanghai 200240, China
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表12种不同金属材料的力学参数

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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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