Measurement method for deformation and contact force of the fuel assembly for China fast reactor under thermal gradient

Wang, Kaiqiang; Chen, Hongen; Shi, Pengpeng; Li, Lijuan; Xie, Shejuan; Chen, Zhenmao; Hei, Baoping; Gao, Fulin; Yang, Hairui; Wu, Yingwei; Su, Guanghui

doi:10.1016/j.anucene.2019.107270

Cited by 2 publications

(1 citation statement)

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“…Fuel assemblies (FAs) are pivotal components in nuclear power plants [1], serving as the primary fuel source to sustain nuclear fission reactions for electricity generation, as illustrated in Figure 1. Exposed to conditions of high temperature, pressure, and radiation, the FAs become irradiated and can become bowed and deformed due to differential growth [2].…”

Section: Introductionmentioning

confidence: 99%

An illumination-robust edge detection method for nuclear fuel assembly deformation measurement

Chen,

Cheng,

Yang

et al. 2024

J. Phys.: Conf. Ser.

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Fuel assemblies (FAs) are critical components of nuclear reactor cores. The excessive deformation of FAs can pose a serious risk of safety accidents in nuclear power plants. Hence, regular monitoring of the deformation of the FAs is necessary. To accomplish this task, the most critical challenge lies in accurately extracting the edges of FAs in the nuclear underwater environment with spatial variations in illumination. In this paper, a novel illumination-robust edge detection method is proposed. Firstly, tilt correction is applied to arrange the fuel rods vertically in the image. Then, the center of each fuel rod is located through grayscale projection and filtered by rod center spacing variance minimization. Subsequently, adaptive thresholding is employed to obtain a binary image. Finally, the edges are identified by searching in the vicinity of rod centers in the binary image. The effectiveness and robustness of the proposed method have been validated in experiments, and the approach has been successfully applied to the FA deformation measurement in nuclear power plants.

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Section: Introductionmentioning

confidence: 99%