计及应力水平效应的复合材料剩余强度概率模型

Probabilistic residual strength model for composite materials considering stress levels

  • 摘要: 针对当前大多数复合材料剩余强度模型通用化程度低、试验成本高的问题,本文提出了一个计及应力水平效应且独立于应力水平的剩余强度概率模型。首先,给出归一化强度储备的定义,并根据归一化强度储备推导出确定性剩余强度模型。然后,将一个疲劳寿命概率模型耦合进确定性剩余强度模型,进而衍生出一个新的剩余强度概率模型。最后,利用文献中的恒幅与变幅剩余强度试验数据对所提出的剩余强度概率模型的准确性和适用性进行验证。结果表明:几乎所有的恒幅试验数据点都分布在预测曲线的95%置信上限与5%置信下限之间,且50%可靠度的预测曲线对试验数据具有高拟合优度值:0.94、0.84及0.97。所提出的模型在充分考虑了复合材料剩余强度统计特征的前提下,仅用一组模型参数即可准确描述多个应力水平下的强度退化。在变幅工况下,所提出模型在升序与降序变幅加载中的预测值与试验值的相对误差均低于6%。

     

    Abstract: To address the problems of low generalization and high testing costs of most current residual strength models for composites, a probabilistic residual strength model that accounts for the effect of stress level and is independent of stress level was proposed. Firstly, the normalized strength reserve was defined and a deterministic residual strength model was derived based on the normalized strength reserve. Then, a fatigue life probability model was coupled into the deterministic residual strength model, and then a new residual strength probability model was derived. Finally, the accuracy and applicability of the proposed probabilistic residual strength model was verified using constant-amplitude and variable-amplitude residual strength experimental data from the open literatures. The results show that almost all the constant amplitude experimental data points are distributed between the upper 95% confidence limit and lower 5% confidence limit of the prediction curves, and the prediction curves with 50% reliability have high goodness-of-fit values for the experimental data: 0.94, 0.84 and 0.97. The proposed model accurately describes strength degradation at multiple stress levels using only one set of model parameters, with sufficient consideration of the statistical characteristics of the residual strength of the composite. The relative error between the predicted values of the proposed model and experimental values for both ascending and descending variable-amplitude loading is less than 6%.

     

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