Chinese Journal of Ship Research, Volume. 18, Issue 5, 50(2023)
Stress prediction of marine diesel engine crankshaft based on digital twin technology
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Table 1. Comparison of eigenvalues of reconstructed cylinder pressure by RBF neural network with measured ones
View tableView in ArticleTable 1. Comparison of eigenvalues of reconstructed cylinder pressure by RBF neural network with measured ones
负荷 爆压 /MPa 爆压位置 /(° CA) 最大压力 上升值/MPa 最大压力上升值 位置/(° CA) 0% 预测值 4.94 363.0 0.18 355.5 实测值 4.81 362.7 0.20 355.5 误差/% 2.70 0.3 1.00 0 25% 预测值 6.13 361.8 0.26 352.0 实测值 6.17 361.8 0.29 351.9 误差/% 0.65 0 1.00 0.1 50% 预测值 7.65 361.8 0.30 354.6 实测值 7.71 361.8 0.31 354.6 误差/% 0.65 0 3.20 0 75% 预测值 9.37 361.8 0.32 352.8 实测值 9.84 360.9 0.31 351.0 误差/% 4.80 0.9 3.20 1.8
Table 2. Properties of crankshaft material 42CrMo
View tableView in ArticleTable 2. Properties of crankshaft material 42CrMo
属性 数值 弹性模量/MPa 2.1×105 泊松比 0.3 密度/( ${\rm{kg \cdot {m} }^{-3}}$ )7850
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Yonghua YU, Meiqiang XIE, Baoyue LI, Ning ZHANG, Xin SONG. Stress prediction of marine diesel engine crankshaft based on digital twin technology[J]. Chinese Journal of Ship Research, 2023, 18(5): 50
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Received: Mar. 31, 2022
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Published Online: Mar. 21, 2025
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