Effect of composition on thermal stability and microstructural behaviour of non-prototype material (CaO-Fe2O3)

The role of simulant materials becomes necessary for the predictive study of the nuclear severe accident phenomena due to its similarity with corium (a liquid form of UO2 and steel). Since simulant material is eco-friendly and has similar properties to corium, it has been widely used in the research field of severe accident management. In this study, material CaO-Fe2O3 a non-eutectic binary mixture is considered for characterization purpose to address the thermophysical properties at different compositional ratios. The CaO-Fe2O3 powder mixture was prepared in mortar for 40 minutes manually to form a homogeneous mixture and then cylindrical pellets prepared at five different ratios with the help of the phase diagram. Further, these pellets were heat-treated at 1200°C for three hours soaking time to address its thermal stability in a programmable electric furnace. Finally, pellets ground into powder form manually for further characterization. Initially, the weight loss analysis was reported by measurement of dimensions of pellets before and after heat treatment. The thermal properties, phase analysis, and morphological studies have been carried out through DSC, XRD and FE-SEM in laboratory and results were discussed in the context of the property of ideal simulant materials used for the study of nuclear severe accidents. The melting point of all the samples were found stable (1200°C-1230°C) and values of activation energy and specific heat were well synchronized between with and without heat-treated samples. Dislocation density of samples increases significantly with increasing the proportion value of calcium oxide after heat treatment.

Introduction of Three Methods Used for the Nuclear Accident Diagnosis

In order to deal with the nuclear severe accidents, the severe accident management systems are popularly considered and developed at home and abroad recently. A severe accident management system usually includes these functional parts: accident monitor, accident diagnosis, accident simulation, accident prognosis and SAMG support. Here, the accident diagnosis part is mainly concerned, and three nuclear accident diagnosis methods are introduced here, including BP neural network method, SDG expert diagnosis technique and artificial diagnosis method, which are also applied to a severe accident management system developed by us. In this paper, firstly, the severe accident management system developed by us will be introduced briefly. Then, three accident diagnosis methods for nuclear power plant (NPP) are showed and described in detail. At last, two cases including LOCA and SGTR accidents are used for the verification of these accident diagnosis methods and some analyzing results and conclusions are given. The results show that the three diagnosis methods are very useful for the accident diagnosis of NPP, which can diagnose the accident type accurately and offer much information or support to the severe accident management system and operators. The paper offers some reference significance for the research of accident diagnosis methods and the development of severe accident management system.