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Feng Gang,Lin Jian,Yang Shuai,Zhang Boxuan,Wang Jiangang,Yang Jia,Xu Zhongfeng,Lei Yongping 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.11
Pressure resistance welding is usually used to seal the connection between the cladding tube and the end plug made of zirconium alloy. The seal welded joint has a direct effect on the service performance of the fuel rod cladding structure. In this paper, the pressure resistance welded joints of zirconium alloy tube-plug structure were obtained by thermal-mechanical simulation experiments. The microstructure and microhardness of the joints were both analyzed. The effect of processing parameters on the microstructure was studied in detail. The results showed that there was no β-Zr phase observed in the joint, and no obvious element segregation. There were different types of Widmanst¨atten structure in the thermo-mechanically affected zone (TMAZ) and heat affected zone (HAZ) of the cladding tube and the end plug joint because of the low cooling rate. Some part of the grains in the joint grew up due to overheating. Its size was about 2.8 times that of the base metal grains. Due to the high dislocation density and texture evolution, the microhardnesses of TMAZ and HAZ were both significantly higher than that of the base metal, and the microhardness of the TMAZ was the highest. With the increasing of welding temperature, the proportion of recrystallization in TMAZ decreased, which was caused by the increasing of strain rate and dislocation annihilation
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Initiating Events Study of the First Extraction Cycle Process in a Model Reprocessing Plant
Wang, Renze,Zhang, Jiangang,Zhuang, Dajie,Feng, Zongyang The Korean Association for Radiation Protection 2016 방사선방어학회지 Vol.41 No.2
Background: Definition and grouping of initiating events (IEs) are important basics for probabilistic safety assessment (PSA). An IE in a spent fuel reprocessing plant (SFRP) is an event that probably leads to the release of dangerous material to jeopardize workers, public and environment. The main difference between SFRPs and nuclear power plants (NPPs) is that hazard materials spread diffusely in a SFRP and radioactive material is just one kind of hazard material. Materials and Methods: Since the research on IEs for NPPs is in-depth around the world, there are several general methods to identify IEs: reference of lists in existence, review of experience feedback, qualitative analysis method, and deductive analysis method. While failure mode and effect analysis (FMEA) is an important qualitative analysis method, master logic diagram (MLD) method is the deductive analysis method. IE identification in SFRPs should be consulted with the experience of NPPs, however the differences between SFRPs and NPPs should be considered seriously. Results and Discussion: The plutonium uranium reduction extraction (Purex) process is adopted by the technics in a model reprocessing plant. The first extraction cycle (FEC) is the pivotal process in the Purex process. Whether the FEC can function safely and steadily would directly influence the production process of the whole plant-production quality. Important facilities of the FEC are installed in the equipment cells (ECs). In this work, IEs in the FEC process were identified and categorized by FMEA and MLD two methods, based on the fact that ECs are containments in the plant. Conclusion: The results show that only two ECs in the FEC do not need to be concerned particularly with safety problems, and criticality, fire and red oil explosion are IEs which should be emphatically analyzed. The results are accordant with the references.
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Intelligent optimization of axial-flow pump using physics-considering machine learning
KANKANAM GAMAGE PIYUMIKA MADUSHANI,Zhou Jie,Feng Jiangang,XUHUI ZHOU,Yuan Zheng,Chen Huixiang,Chen Jinbo 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
To address the significant energy waste generated by axial flow pumps, this paper proposes an intelligent optimization method based on physics-considering machine learning. First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on back-propagation neural network and the encoding layers of random sampling and local feature aggregator network created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%. Compared with the preliminary solution, the efficiency of the optimized axial flow pump is increased by 1.615%, with a wider high-efficiency range and an optimal operating point closer to the design conditions. Overall, this intelligent optimization method has the potential to significantly reduce the design time of axial pumps and increase their performance.
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