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Coarse-grained soil (CGS) has been extensively used in engineering practices because of their advantages of relatively high strength, easy compaction and small deformation. In engineering design, CGS is usually regarded as a non-liquefiable soil. However, many post-earthquake surveys have shown that CGS, such as sandy gravel, can be seriously liquefied, meaning that under certain seismic loads, CGS with loose to moderate densities might have the potential to be liquefied. In this study, soil-water coupling finite difference-finite element (FD-FE) deformation analyses, based on the cyclic mobility (CM) model, were conducted to investigate the monotonic and cyclic characteristics of CGS. The model can accurately describe the complicated mechanical behaviour of CGS, considering the influences of the stress-induced anisotropy, the density and the naturally deposited structure of soils in a unified way. The material parameters of CGS were calibrated by large-scale triaxial tests. Particularly, the influences of non-uniform factors on the element behaviour of CGS subjected to monotonic/cyclic loading were systematically studied, such as the gravitational stress field (GSF) of the specimens, the friction between the loading plate and specimen (FLPS), the confining pressure, the cyclic shear stress ratio ( CSR ), and the loading frequency, etc. It is found that the CM model can reproduce the stress-strain-dilatancy relation of CGS in the CU ‾ and CD tests well and that the GSF and FLPS have a certain impact on the element behaviour of CGS. However the average value results from the numerical simulations can quantitatively and qualitatively describe the monotonic/cyclic mechanical behaviour. In addition, under lower confining pressure or higher CSR, the non-uniform distribution of volumetric and axial strain inside the specimen will be prominent, and the specimen enters the cyclic-mobility stage more easily. High loading frequency and CSR will cause a large distortion of the specimen, at which point the element test, strictly speaking, is no longer an element test. [ABSTRACT FROM AUTHOR]

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators. [ABSTRACT FROM AUTHOR]

Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the elastic support of thrust bearings. The dynamic characteristics of the shafting system are studied. Firstly, the multi-DOF transfer matrix expressions of the simplified mass element and spring element are derived. A three-DOF transfer matrix-coupling model in XOZ plane is established by using force coupling conditions at thrust bearings. Then, a test rig is built to test the propulsion shafting. The bending vibration characteristics of the shafting under the support conditions of thrust bearings are studied. And finally, the transmission characteristics of vertical and longitudinal forces of propellers under different support conditions of thrust bearings are emphatically analyzed. [ABSTRACT FROM AUTHOR]