Haiyang Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Haiyang Liu has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Chen Wang
Suzhou University of Science and Technology,School of Mathematics and Physics,Suzhou,China
1 shared publication - Fei Liu
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences 1 , Hefei 230031, China
1 shared publication - Feng Wu
Changzhou Institute of Technology,School of Electrical Information Engineering,Changzhou,China
1 shared publication - Jinxing Zheng
Chinese Academy of Sciences
1 shared publication - Lei Zhu
Institute of Plasma Physics, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences 1 , Hefei 230031
1 shared publication - Ming Li
Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, China
1 shared publication
Latest Publications
- 4 March 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Applied Superconductivity
- Vol. 34 (4) , 1-9
Parametric Optimization of CFETR Toroidal Field Superconducting Magnet Gravity Support
China Fusion Engineering Test Reactor (CFETR) constitutes a fully superconducting demonstration fusion reactor, primarily designed and developed by China. The gravity support (GS) in the Toroidal Field (TF) superconducting magnet assumes a critical role in withstanding substantial loads, encompassing the gravity of the superconducting magnet system, electromagnetic forces, and thermal loads. To attain an optimal GS structure, a parametric optimization of the GS is undertaken. This paper commences with an elucidation of the GS structure. The flexible plate structure (FPS) is designed to facilitate the radial thermal expansion of the TF magnet. Additionally, a thermal anchor structure is incorporated to alleviate the load on the cryogenic system. Subsequently, the finite element model (FEM) is explicated, encompassing details on mesh configuration, material properties, load distribution, and boundary conditions. The subsequent section delineates the optimization efforts focused on the FP. A parametric FEM is established, and a response surface methodology is employed to delineate the relationship between design variables and the desired objectives. Through the optimization process, a noteworthy reduction of 14.6% in stress intensity and a consequential 6.7% decrease in heat leakage are achieved. Finally, the fatigue analysis is accomplished by utilizing SN approach. The fatigue life of the entire set of FPS satisfy the design criteria.
- 4 March 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Applied Superconductivity
- Vol. 34 (4) , 1-9
Parametric Optimization of CFETR Toroidal Field Superconducting Magnet Gravity Support
China Fusion Engineering Test Reactor (CFETR) constitutes a fully superconducting demonstration fusion reactor, primarily designed and developed by China. The gravity support (GS) in the Toroidal Field (TF) superconducting magnet assumes a critical role in withstanding substantial loads, encompassing the gravity of the superconducting magnet system, electromagnetic forces, and thermal loads. To attain an optimal GS structure, a parametric optimization of the GS is undertaken. This paper commences with an elucidation of the GS structure. The flexible plate structure (FPS) is designed to facilitate the radial thermal expansion of the TF magnet. Additionally, a thermal anchor structure is incorporated to alleviate the load on the cryogenic system. Subsequently, the finite element model (FEM) is explicated, encompassing details on mesh configuration, material properties, load distribution, and boundary conditions. The subsequent section delineates the optimization efforts focused on the FP. A parametric FEM is established, and a response surface methodology is employed to delineate the relationship between design variables and the desired objectives. Through the optimization process, a noteworthy reduction of 14.6% in stress intensity and a consequential 6.7% decrease in heat leakage are achieved. Finally, the fatigue analysis is accomplished by utilizing SN approach. The fatigue life of the entire set of FPS satisfy the design criteria.
- 15 July 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 61, 358-362
Design of Fast Star Image Extraction Module for Autonomous Star Sensors Based on FPGA
Measuring attitudes of spacecraft accurately is required by the modern space explorations. Autonomous star sensors are one of the most advanced devices to measure attitudes as good as arc-seconds. In a measurement cycle, there are mainly four steps, i.e. star imaging via cameras, star image extraction, star pattern identification and attitude estimate among which star image extraction spends far longer time than the other steps since the star maps usually have very high resolution in order to achieve a very high precision. In this paper, the nearly real-time star image extraction module which utilized the chip of ZYNQ-XC7Z010-CLG400-1 as the core of the processor developed by Xilinx Inc. was designed based on FPGA when considering noise disturbances. First, the basic theory of the star image extraction was introduced. Then, the hardware structure was described. The program to process image maps and extract star images was designed. Star maps were processed by the global threshold segment method after the threshold of the intensity was calculated by OTSU algorithm. Positions of the star image centroids were calculated via connected domain analysis and the centroid calculation after the star map was processed by morphology processing. The procedures of the star image extraction were described in detail. Finally, the design results of the star image extraction module were presented. Experiments were fulfilled when simulated star maps with Gaussian noise were input into the module. It proved that the developed module of star image extraction extracted the star images correctly. As for the star maps with 1024×1024 pixels, the average processing time was less than 25ms. The position accuracy was superior to 1/18 pixels.
- 15 July 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 61, 358-362
Design of Fast Star Image Extraction Module for Autonomous Star Sensors Based on FPGA
Measuring attitudes of spacecraft accurately is required by the modern space explorations. Autonomous star sensors are one of the most advanced devices to measure attitudes as good as arc-seconds. In a measurement cycle, there are mainly four steps, i.e. star imaging via cameras, star image extraction, star pattern identification and attitude estimate among which star image extraction spends far longer time than the other steps since the star maps usually have very high resolution in order to achieve a very high precision. In this paper, the nearly real-time star image extraction module which utilized the chip of ZYNQ-XC7Z010-CLG400-1 as the core of the processor developed by Xilinx Inc. was designed based on FPGA when considering noise disturbances. First, the basic theory of the star image extraction was introduced. Then, the hardware structure was described. The program to process image maps and extract star images was designed. Star maps were processed by the global threshold segment method after the threshold of the intensity was calculated by OTSU algorithm. Positions of the star image centroids were calculated via connected domain analysis and the centroid calculation after the star map was processed by morphology processing. The procedures of the star image extraction were described in detail. Finally, the design results of the star image extraction module were presented. Experiments were fulfilled when simulated star maps with Gaussian noise were input into the module. It proved that the developed module of star image extraction extracted the star images correctly. As for the star maps with 1024×1024 pixels, the average processing time was less than 25ms. The position accuracy was superior to 1/18 pixels.