Accurate prediction of structural degradation in diesel engine cylinder blocks based on component scaling methods
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ID: 309992
2025
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Abstract
This paper proposes a method that significantly improves the prediction accuracy of structural degradation in the main bearing wall of diesel engine cylinders. Firstly, based on the component scaling method, a scaling study is conducted on the main bearing wall to obtain a scaled model of the main bearing wall. By performing crack growth rate (da/dN) tests and threshold value (∆Kth) tests on the scaled model, accurate da/dN and ∆Kth data for the main bearing wall are indirectly obtained. Based on this, an accurate da/dN model for the main bearing wall, considering structural and load factors, is constructed, and the accuracy of the scaled model is verified by introducing standard single-edge notched bend (SENB) specimens for comparison. Secondly, based on the scaled model and the da/dN model measured from SENB specimens, structural degradation prediction studies are conducted on the main bearing wall, establishing two prediction models for the structural degradation of the main bearing wall. Finally, fatigue tests are conducted on the main bearing wall to verify the accuracy of the structural degradation prediction model built from the scaled model. Simultaneously, microscopic characterization studies are conducted on the fracture surface of the main bearing wall to determine the microscopic failure mechanism. Fatigue test verification shows that the fracture mode of the main bearing wall is primarily ductile fracture dominated by dimple fracture. The structural degradation prediction model for the main bearing wall built from the scaled model, which fully considers the structural and load factors of the main bearing wall, can more accurately reflect the structural degradation of the main bearing wall compared to traditional SENB specimens.
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imported_1768911197_696f715dd9183
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| Authors | Fu, Yixuan |
| Journal | Frontiers in surgery |
| Year | 2025 |
| DOI |
10.3389/fpace.2025.1604213
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| URL | |
| Keywords | Keywords not found |
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