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<p>The high-performance design and fabrication of crankshafts are crucial for the reliability and service life of marine medium-speed diesel engine (MMDE), ensuring efficient operation and safety of vessels. This study proposes an innovative method for predicting fatigue life and optimizing the key structural dimension of MMDE crankshafts integrated with Kriging model and particle swarm optimization algorithm. Initially, based on the ABAQUS secondary development program, an integrated plugin suitable for the CHD416 L6 crankshaft has been developed. This plugin incorporates functions such as parametric modeling, automatic assignment of material properties, mesh generation, and rapid implementation of crankshaft bending analysis. Secondly, a comprehensive bending fatigue experimental platform for the CHD416 L6 crankshaft is established to validate the accuracy of the fatigue simulation model by assessing bending stress fatigue limits. Finally, a command stream-driven method is introduced to rapidly predict the fatigue life of crankshafts, enabling the efficient generation of a large dataset of samples. The integration of the Kriging surrogate model with the particle swarm optimization algorithm optimizes key crankshaft structural dimensions. The results indicate that the bending moment is the primary factor affecting the fatigue life of crankshaft under complex operating conditions. Additionally, the structural dimensions of the crank pin substantially influence the fatigue life of the optimized crankshaft. Notably, the fatigue life of the optimized crankshaft structure is increased by 118% compared to the initial design model.</p>

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Fatigue life prediction and optimization design of marine medium-speed diesel engine crankshaft based on kriging surrogate model

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Yong Liu,
  • Meng Zhu,
  • Haoan Chen,
  • Guochao Li,
  • Honggen Zhou,
  • Shufei Xue

摘要

The high-performance design and fabrication of crankshafts are crucial for the reliability and service life of marine medium-speed diesel engine (MMDE), ensuring efficient operation and safety of vessels. This study proposes an innovative method for predicting fatigue life and optimizing the key structural dimension of MMDE crankshafts integrated with Kriging model and particle swarm optimization algorithm. Initially, based on the ABAQUS secondary development program, an integrated plugin suitable for the CHD416 L6 crankshaft has been developed. This plugin incorporates functions such as parametric modeling, automatic assignment of material properties, mesh generation, and rapid implementation of crankshaft bending analysis. Secondly, a comprehensive bending fatigue experimental platform for the CHD416 L6 crankshaft is established to validate the accuracy of the fatigue simulation model by assessing bending stress fatigue limits. Finally, a command stream-driven method is introduced to rapidly predict the fatigue life of crankshafts, enabling the efficient generation of a large dataset of samples. The integration of the Kriging surrogate model with the particle swarm optimization algorithm optimizes key crankshaft structural dimensions. The results indicate that the bending moment is the primary factor affecting the fatigue life of crankshaft under complex operating conditions. Additionally, the structural dimensions of the crank pin substantially influence the fatigue life of the optimized crankshaft. Notably, the fatigue life of the optimized crankshaft structure is increased by 118% compared to the initial design model.