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<p>This work introduces an augmented nonlinear damage accumulation framework that hierarchically advances the Peng Yue (Yue) model—a refined extension of the Manson–Halford (M-H) framework. The embedded operator inherently resolves load–interaction&#xa0;effects. Rigorous validation through comparative analysis against established benchmarks confirms the framework’s superior predictive accuracy for fatigue life estimation, with performance significantly exceeding both the foundational M-H model and its derivative Yue model. Notably, under five-level loading spectra, the proposed framework achieves 60.12% longer predicted fatigue life than the Yue benchmark. For an aluminum alloy subjected to four-level decreasing load blocks, the improved model demonstrates a 166.9% enhancement in predicted fatigue life over the classical M-H formulation. These results underscore the framework's efficacy in handling complex variable amplitude loading scenarios, positioning it as a robust tool for high-fidelity fatigue life assessment in engineering applications.</p> Graphical abstract <p></p>

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A load–interaction enhanced Peng Yue model for nonlinear fatigue damage

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Zexiao Han,
  • Junting Zhang,
  • Chongbo Li,
  • Kaihui Ma,
  • Yuanji Xu

摘要

This work introduces an augmented nonlinear damage accumulation framework that hierarchically advances the Peng Yue (Yue) model—a refined extension of the Manson–Halford (M-H) framework. The embedded operator inherently resolves load–interaction effects. Rigorous validation through comparative analysis against established benchmarks confirms the framework’s superior predictive accuracy for fatigue life estimation, with performance significantly exceeding both the foundational M-H model and its derivative Yue model. Notably, under five-level loading spectra, the proposed framework achieves 60.12% longer predicted fatigue life than the Yue benchmark. For an aluminum alloy subjected to four-level decreasing load blocks, the improved model demonstrates a 166.9% enhancement in predicted fatigue life over the classical M-H formulation. These results underscore the framework's efficacy in handling complex variable amplitude loading scenarios, positioning it as a robust tool for high-fidelity fatigue life assessment in engineering applications.

Graphical abstract