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Study on In-Service Inspection of Nuclear Fuel Assembly Failure Using Ultrasonic Plate Wave.

Xiang XiaoGuo Zheng ZhouKe Qing WangFeng XiKun Zeng
Published in: Sensors (Basel, Switzerland) (2022)
As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter into the fuel rod, which may lead to the failure of the fuel assembly. However, current diagnostic methods have limitations due to the special structure of the fuel assembly and the underwater and radioactive environment. In this paper, a novel inspection method is proposed to recognize the failure of a fuel rod. The fuel rod failure can be detected based on the presence or absence of coolant inside the fuel rod by using an ultrasonic plate wave. The inspection model and process algorithm are proposed for in-service inspection. The relationship between signal and scanning position is established and analyzed. Both ultrasound field simulation and experiment have been carried out for validation. The corresponding results illustrate that the failed nuclear fuel rod of the whole fuel assembly (including the internal rods) can be effectively detected without the influence of the near-field region by using the proposed method.
Keyphrases
  • magnetic resonance imaging
  • machine learning
  • deep learning
  • neural network
  • clinical evaluation