Modeling of irradiation-induced damage and failure behaviors of fuel foil/cladding interface in UMo/Zr monolithic fuel plates

Models to describe the damage and fracture behaviors of the interface between the fuel foil and cladding in UMo/Zr monolithic fuel plates were established and numerically implemented.The effects of the interfacial cohesive strength and cohesive energy on the irradiationinduced thermal-mechanical behaviors of fuel plates were investigated.The results indicated that for heterogeneously irradiated fuel plates:(1)interfacial damage and failure were predicted to be initiated near the fuel foil corner with higher fission densities,accompanied by the formation of a large gap after interface failure,which was consistent with some experimental observations;high tensile stresses in the fuel foil occurred near the edges of the failed interface,attributed to through-thickness cracking of the fuel foil,as found in some post-irradiation examinations;(2)the cohesive strength and cohesive energy of the interface both influenced the in-pile evolution behaviors of fuel plates;a lower cohesive strength or cohesive energy resulted in faster interfacial damage;(3)after interface fracture,the thickness of the whole plate increased to a greater degree(by~20%)than that of the samples without interfacial damage,which was attributed to the locally enhanced Mises stresses and the nearby creep deformations around the cracked interface.This study provided a theoretical basis for assessing failure in fuel elements.

Identify information sources with different start times in complex networks based on sparse observers

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial moments.Although there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains unresolved.In this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start times.The proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread duration.The core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear programming.Extensive experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and efficiency.Furthermore,we find that our method maintains robustness irrespective of the number of sources and the average degree of network.Compared with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.