ABAQUS analysis of the SiC cladding fuel rod behavior under PWR normal operation conditions
Source abstract
Abstract
Abstract Silicon Carbide (SiC) cladding is anticipated to be superior to zirconium-based cladding with regard to tolerating light water reactor (LWR) accidents, but it is also crucial that the cladding performance under normal operation conditions be well understood. This paper focuses on the modeling of thermomechanical behavior of a two-layer SiC cladding in Pressurized Water Reactor (PWR) fuel rod under normal operation conditions. For this purpose, a set of ABAQUS subroutines has been written to incorporate material properties and behavior models of Chemical Vapor Deposited (CVD) monolithic SiC, Chemical Vapor Infiltration (CVI) SiCf/SiC Ceramic Matrix Composite (CMC) and UO2. Especially, apart from UO2 irradiation behaviors and fuel rod phenomena models (gas pressure, gap heat transfer, etc.) which have been extensively studied, an alternative swelling model of SiC dedicated for transient simulation and a pseudo-ductile behavior model of CVI SiCf/SiC have been implemented. Both models are important for conducting best estimate analysis of the fuel rod in-pile behavior. Then after benchmarking with the BISON fuel performance analysis code, the ABAQUS model is applied to a downscaled PWR fuel rod with two-layer SiC cladding design, considering different cladding thicknesses and covering the conditions of simple constant power levels, power ramps, power operation mode, reactor hot standby and cold shutdown. The effect of swelling differential between CVD SiC and CVI SiCf/SiC is also discussed. With the help of finite element analysis (FEA), the tightly coupled thermomechanical behavior of SiC cladding fuel rod can be well studied.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nuclear Materials and Properties · Advanced ceramic materials synthesis · Fusion materials and technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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