A novel approach has been developed to estimate fatigue life at the specimen/component level from the simulation of relatively small Representative Volume Elements (RVE) of the polycrystalline microstructure. This technique estimates the statistical distribution of fatigue lives under general multiaxial loading conditions accounting for both microstructure and specimen size. The model relies on computational homogeneization where the crystal behavior follows a crystal plasticity model and simulations are performed using a FFT based solver. The simulation of the cyclic response of a set of different RVEs provides the statistical distribution of Fatigue Indicator Parameters for that RVE size, which can be accurately represented by an extreme value distribution as the Gumbel distribution. This distribution is upscaled to the actual size of the specimen or component of interest using a weakest link approach and is finally transformed into a distribution of fatigue lives using a simple fatigue-life expression fitted with experiments. The framework proposed estimates fatigue lives of specimens with millions of grains from the results obtained with RVEs containing only hundreds of grains and is able to reproduce the specimen size effect on fatigue life. The approach is first numerically validated and then used to predict the statistical distribution of fatigue life of a polycrystalline Ni-based superalloy showing very accurate results.

中文翻译：

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基于微机械疲劳的升级方法：从 RVE 到试样和部件寿命预测

已经开发出一种新方法来通过模拟相对较小的多晶显微组织的代表性体积元素 (RVE) 来估计试样/组件级别的疲劳寿命。该技术在考虑微观结构和试样尺寸的一般多轴载荷条件下估计疲劳寿命的统计分布。该模型依赖于计算均质化，其中晶体行为遵循晶体塑性模型，并使用基于 FFT 的求解器进行模拟。对一组不同 RVE 的循环响应的模拟提供了该 RVE 大小的疲劳指标参数的统计分布，可以准确地表示为 Gumbel 分布的极值分布。使用最弱链接方法将该分布放大到样品或感兴趣组件的实际尺寸，并最终使用符合实验的简单疲劳寿命表达式转换为疲劳寿命分布。提出的框架从仅包含数百个晶粒的 RVE 获得的结果中估计具有数百万个晶粒的试样的疲劳寿命，并且能够再现试样尺寸对疲劳寿命的影响。该方法首先经过数值验证，然后用于预测多晶镍基高温合金疲劳寿命的统计分布，显示出非常准确的结果。提出的框架从仅包含数百个晶粒的 RVE 获得的结果中估计具有数百万个晶粒的试样的疲劳寿命，并且能够再现试样尺寸对疲劳寿命的影响。该方法首先经过数值验证，然后用于预测多晶镍基高温合金疲劳寿命的统计分布，显示出非常准确的结果。提出的框架从仅包含数百个晶粒的 RVE 获得的结果中估计具有数百万个晶粒的试样的疲劳寿命，并且能够再现试样尺寸对疲劳寿命的影响。该方法首先经过数值验证，然后用于预测多晶镍基高温合金疲劳寿命的统计分布，结果非常准确。