Zimmer Cyril, Rallabandi Yashashwini Nikhitha, Szielasko Klaus, Eichheimer Christian, Luke Michael, Youssef Sargon
Fraunhofer Institute for Nondestructive Testing IZFP, Campus E3 1, 66123 Saarbrücken, Germany.
Fraunhofer Institute for Mechanics of Materials IWM, Wöhlerstraße 11, 79108 Freiburg, Germany.
Materials (Basel). 2021 Sep 13;14(18):5258. doi: 10.3390/ma14185258.
Reactor safety research aims at the safe operation of nuclear power plants during their service life. In this respect, Fraunhofer IZFP's micromagnetic multiparameter, microstructure, and stress analysis (3MA) has already made a significant contribution to the understanding of different aging mechanisms of component materials and their characterization. The basis of 3MA is the fact that microstructure and mechanical stress determine both the mechanical and magnetic material behavior. The correlation between features of magnetic and mechanical material behavior enables the micromagnetic prediction of mechanical properties and stress, both of which can decisively influence the service life. The Federal Ministry for Economic Affairs and Energy (BMWi) funded this research, handling the mutually superimposed microstructural and stress-dependent influences, a substantial challenge, especially under practical conditions. This superposition leads to ambiguities in the micromagnetic features. The 3MA testing system has been extended by more sophisticated evaluation methods being able to cope with more complex datasets. Investigations dealing with the expansion of the feature extraction and machine learning methods have led to a more precise distinction between microstructural and stress-dependent influences. This approach provides the basis for future applications in reactor safety.
反应堆安全研究旨在确保核电站在其使用寿命期间安全运行。在这方面,弗劳恩霍夫IZFP的微磁多参数、微观结构和应力分析(3MA)已经为理解部件材料的不同老化机制及其特性做出了重大贡献。3MA的基础是微观结构和机械应力决定了材料的机械和磁行为这一事实。磁性和机械材料行为特征之间的相关性使得能够对机械性能和应力进行微磁预测,而这两者都可能对使用寿命产生决定性影响。联邦经济事务和能源部(BMWi)资助了这项研究,该研究应对相互叠加的微观结构和应力相关影响,这是一项重大挑战,尤其是在实际条件下。这种叠加导致微磁特征存在模糊性。3MA测试系统通过更复杂的评估方法得到了扩展,这些方法能够处理更复杂的数据集。对特征提取和机器学习方法扩展的研究使得能够更精确地区分微观结构和应力相关影响。这种方法为反应堆安全的未来应用奠定了基础。