The thermo-mechanical-chemical coupling effect presents significant challenges in accurately predicting the fatigue life of 316 austenitic stainless steel in high-temperature and high-pressure water environments (referred to hereafter as environmental fatigue). The complexity of environmental fatigue experiments results in limited and dispersed data, further making the life prediction difficult. Traditional fatigue life prediction models are often constrained by specific loading conditions and do not adequately account for the complex environmental influences. To address these issues, this paper proposes a novel environmental fatigue life prediction model of 316 stainless steel utilizing conditional Generative Adversarial Networks. The proposed model incorporates critical environmental factors, loading conditions and stacking fault energy, allowing direct prediction of environmental fatigue life. A comparative analysis on the predicted and experimental results reveals that the cGAN-based model significantly improves the prediction accuracy, reducing the fatigue life prediction error from a factor of 5 to within 3. To quantify the uncertainty in fatigue life prediction, the Monte Carlo Dropout method is employed to enable a probabilistic assessment of fatigue life. Furthermore, four environmental and loading conditions are established to evaluate the model’s extrapolation capability. The results demonstrate that the probabilistic fatigue assessment effectively captures data distribution and achieves high prediction accuracy.

中文翻译：

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基于 cGAN 的 316 奥氏体不锈钢在高温高压水环境中的疲劳寿命预测


热-机械-化学耦合效应对准确预测 316 奥氏体不锈钢在高温高压水环境中的疲劳寿命（以下简称环境疲劳）提出了重大挑战。环境疲劳实验的复杂性导致数据有限且分散，进一步使寿命预测变得困难。传统的疲劳寿命预测模型通常受到特定载荷条件的限制，并且没有充分考虑复杂的环境影响。为了解决这些问题，本文提出了一种利用条件生成对抗网络的 316 不锈钢的新型环境疲劳寿命预测模型。所提出的模型结合了关键环境因素、负载条件和堆叠故障能量，可以直接预测环境疲劳寿命。对预测结果和实验结果的比较分析表明，基于 cGAN 的模型显著提高了预测精度，将疲劳寿命预测误差从 5 倍降低到 3 倍以内。为了量化疲劳寿命预测的不确定性，采用蒙特卡洛 Dropout 方法对疲劳寿命进行概率评估。此外，建立了四个环境和载荷条件来评估模型的外推能力。结果表明，概率疲劳评估有效地捕获了数据分布并实现了较高的预测精度。