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Prediction of Nozzle Erosion Wear Based on Machine Learning Algorithm
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Ruo-wen LI, Shao-hu LIU*, Ze-qing XU, Suo-nan WANG
Science Technology and Engineering | 2025, 25(11) : 4526 - 4533
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Science Technology and Engineering | 2025, 25(11): 4526-4533
Papers·Petroleum and Natural Gas Industry
Prediction of Nozzle Erosion Wear Based on Machine Learning Algorithm
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Ruo-wen LI, Shao-hu LIU*, Ze-qing XU, Suo-nan WANG
Affiliations
  • School of Mechanical Engineering, Yangtze University, Jingzhou 434023, China
Published: 2025-04-18 doi: 10.12404/j.issn.1671-1815.2402953
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After fracturing, the solid particles carried by the high speed liquid will cause serious erosion to the oil nozzle, and it is difficult to ensure the stable operation of the oil nozzle. To address the serious erosion problem of the nozzle, numerical simulation was employed to study the erosion wear of the nozzle, and the influence patterns of sand content, sand grain diameter, sand grain density, pump displacement, and liquid viscosity on the erosion wear of the nozzle were analyzed. The research indicates that: when the sand content and liquid viscosity increase, the maximum erosion rate exhibits linear growth; when the sand grain density and pump displacement increase, the maximum erosion rate exhibits exponential growth; and when the sand grain diameter increases, the maximum erosion rate shows exponential decrease. The orthogonal test method is used to judge the significance of each factor. The factors affecting the erosion wear of the nozzle are as follows: sand content ratio > pump displacement > sand density > sand diameter > liquid viscosity.Based on the results of numerical simulation, the machine learning method is used to compare and analyze SVR(support vector regression), CNN(convolutional neural network), BP(back propagation) neural network and RFR(random forest regression) algorithm to predict the erosion wear results of oil nozzle respectively. By preferring the SVR algorithm and adopting the particle swarm optimization algorithm to optimize the prediction model, a better nozzle erosion prediction model is obtained.

nozzle  /  erosion wear  /  orthogonal test  /  model optimization  /  machine learning
Ruo-wen LI, Shao-hu LIU, Ze-qing XU, Suo-nan WANG. Prediction of Nozzle Erosion Wear Based on Machine Learning Algorithm[J]. Science Technology and Engineering, 2025 , 25 (11) : 4526 -4533 . DOI: 10.12404/j.issn.1671-1815.2402953
Year 2025 volume 25 Issue 11
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doi: 10.12404/j.issn.1671-1815.2402953
  • Receive Date:2024-04-22
  • Online Date:2025-07-09
  • Published:2025-04-18
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  • Received:2024-04-22
  • Revised:2024-07-30
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    School of Mechanical Engineering, Yangtze University, Jingzhou 434023, 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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