The fatigue cracks in a fighter aircraft that pose the greatest threat to structural integrity and availability are usually less than 1 mm for most of their lives. However, it has long been recognised that small cracks can grow significantly faster than long cracks at the same stress intensity range (). This means predictions for small cracks growing under realistic spectrum loading can be significantly non-conservative when using linear elastic fracture mechanics and long crack-based empirical rate data. Previous work showed empirical rate data based on small cracks grown under constant amplitude (CA) loading could significantly improve predictions. However, such data still have limitations related to an inability to predict the spectrum history effects that influence small cracks. This paper presents a method to optimise a model to predict fatigue crack growth rates for cracks loaded with realistic spectra. This method is demonstrated for small cracks in Aluminium Alloy 7050-T7451 grown under fighter wing root spectrum loading. Numerical optimisation is used to select model parameters that minimise fatigue crack growth rate prediction errors for a training dataset. This purpose-built model consistently outperformed a generic model based on the growth of small cracks under CA loading.

中文翻译：

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优化变幅载荷下小裂纹的疲劳裂纹扩展预测

战斗机中对结构完整性和可用性构成最大威胁的疲劳裂纹在其大部分使用寿命内通常小于 1 毫米。然而，人们早已认识到，在相同的应力强度范围内，小裂纹的生长速度明显快于长裂纹 ()。这意味着当使用线弹性断裂力学和基于长裂纹的经验速率数据时，对在实际谱载荷下生长的小裂纹的预测可能明显不保守。先前的工作表明，基于等幅 (CA) 载荷下生长的小裂纹的经验速率数据可以显着改善预测。然而，此类数据仍然存在局限性，无法预测影响小裂纹的频谱历史效应。本文提出了一种优化模型的方法，以预测加载真实光谱的裂纹的疲劳裂纹扩展速率。该方法针对战斗机机翼根谱载荷下铝合金 7050-T7451 中生长的小裂纹进行了验证。数值优化用于选择模型参数，最大限度地减少训练数据集的疲劳裂纹扩展速率预测误差。这种专门构建的模型始终优于基于 CA 载荷下小裂纹生长的通用模型。