在役设备和辐照材料的断裂韧性可以采用环形缺口小冲杆试样获取.采用内聚力模型描述T91钢的韧性断裂行为和裂纹扩展过程,并以反向有限元法标定模型所需的两个材料参数.反向有限元法的成功实现需要断裂损伤阶段的载荷位移曲线对两个模型参数较为敏感,可以通过样品和缺口的几何尺寸加以优化.研究了试样直径与厚度的比例、试样缺口深度以及有无预制裂纹3个因素对参数敏感性的影响,得到缺口样品的优化设计.在此基础上,选取两组参数进行有限元模拟,得到载荷位移曲线.以此曲线作为逼近目标,采用遗传算法和随机游走算法进行反向有限元迭代拟合,提取内聚力模型参数.计算结果表明.所得参数与预先选取的参数误差为1%以内,验证了样品设计的灵敏度和反向有限元法的准确性.

The fracture toughness of in-service equipment and irradiation materials can be obtained from the ring-notched small punch specimen. The cohesive model was used to describe the ductile fracture behavior and crack propagation process of T91 steel, and the two parameters of material required by this model were calibrated by using the inverse finite element method. The successful implementation of this method requires that the load-displacement curve in the fracture damage stage is sensitive to the two model parameters, which can be optimized by the geometric design of the specimen and notch. The influence of the ratio of diameter to thickness, the depth of notch, and the presence or absence of prefabricated cracks on parameter sensitivity was studied, and the optimized design of the ring-notched specimen was obtained, based on which, a set of parameters was selected for finite element simulation to obtain the load-displacement curve. This curve was chosen as the target, and the genetic algorithm and the random walk algorithm were used for iterative fitting by using the inverse finite element method to extract the parameters of the cohesive model. The calculated results show that the error between the obtained parameters and the pre-selected parameters is less than 1%, which verifies the sensitivity of the specimen design and the accuracy of the inverse finite element method.