In the present work, a numerical model based on the coupling of Kampmann and Wagner Numerical (KWN) framework and thermodynamic software ThermoCalc has been developed to predict grain boundary precipitate evolution in 316H stainless steel during thermal aging. The model is calibrated and validated against precipitate size distributions obtained by accelerated isothermal heat treatment and analysed using scanning electron microscopy (SEM). Elemental distribution was also investigated using electron microprobe analysis (EPMA). The predicted average particle size, particle size distribution and precipitate number density predicted by the model were found to be in good agreement with the experimental results. The model was then applied to predict the particle size distribution after several years exposure at service temperature. It is demonstrated that these predictions are consistent with measurements from a service-exposed part. The sensitivity of the precipitate size distribution to temperature is emphasised, and it is demonstrated that the model has potential as a useful tool for predicting evolution of the precipitate size distribution during service, providing reliable thermal data are available for the whole service life.

在目前的工作中，基于坎普曼和瓦格纳数值（KWN）框架以及热力学软件ThermoCalc的耦合数值模型已经开发出来，可以预测316H不锈钢在热时效过程中晶界析出物的演变。对该模型进行了校准，并针对通过加速等温热处理获得的沉淀物尺寸分布进行了验证，并使用扫描电子显微镜（SEM）对其进行了分析。元素分布也使用电子微探针分析（EPMA）进行了研究。该模型预测的平均粒径，粒径分布和沉淀物数量密度与实验结果吻合良好。然后将该模型用于预测在使用温度下暴露数年后的粒度分布。事实证明，这些预测与服务暴露部分的测量结果一致。强调了沉淀物尺寸分布对温度的敏感性，并证明了该模型具有潜力，可用于预测服役期间沉淀物尺寸分布的演变，提供可靠的热数据，可用于整个使用寿命。