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<p>This research investigated fatigue failure in LPST blade, crucial for maintaining turbine reliability. A hybrid approach combining ANSYS and FRANC3D was employed to simulate crack propagation in an X10CrNiMoV12-2-2 alloy blade. A sub-model with a 2 mm edge crack and template radius of 0.5 mm was created using the rubber box method in FRANC3D. Initially, the crack propagated to 6.5 mm, and stress intensity factors (SIFs) were computed over 13 steps. The SIF, K<sub>I</sub> quantified the driving force behind crack propagation with fatigue life reaching 38414 cycles. Further loading led to a maximum K<sub>I</sub> of 3640 MPa√mm at the 86<sup>th</sup> step, surpassing the material’s fracture toughness. This transition to unstable crack growth regime commenced after 254123 cycles, establishing a narrow transition window of approximately 2955 cycles. The finding highlights the critical need for precise modeling to predict fatigue and prevent catastrophic blade failure.</p>

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Hybrid computational approach for crack growth simulation and fatigue life prediction in low-pressure steam turbine blades

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Nitin Sehra,
  • Sushila Rani

摘要

This research investigated fatigue failure in LPST blade, crucial for maintaining turbine reliability. A hybrid approach combining ANSYS and FRANC3D was employed to simulate crack propagation in an X10CrNiMoV12-2-2 alloy blade. A sub-model with a 2 mm edge crack and template radius of 0.5 mm was created using the rubber box method in FRANC3D. Initially, the crack propagated to 6.5 mm, and stress intensity factors (SIFs) were computed over 13 steps. The SIF, KI quantified the driving force behind crack propagation with fatigue life reaching 38414 cycles. Further loading led to a maximum KI of 3640 MPa√mm at the 86th step, surpassing the material’s fracture toughness. This transition to unstable crack growth regime commenced after 254123 cycles, establishing a narrow transition window of approximately 2955 cycles. The finding highlights the critical need for precise modeling to predict fatigue and prevent catastrophic blade failure.