Z-FFR reliability analysis by big data in collaboration with artificial intelligence and industrial software

Liu, Gaoyang; Liu, Pan; Xiong, Wenbin; Shi, Yan; Tang, Zhangchun; Gao, Qiang; Qu, Fanyu; Liu, Cheng

doi:10.1117/12.2674856

Cited by 3 publications

References 6 publications

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Machine learning-based 2D model optimization design for fusion target pellets

Liu,

Wang,

Yuan

2024

J. Phys.: Conf. Ser.

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This paper proposes a machine learning-based method for the optimal design of a two-dimensional model of a fusion target pellet. The optimized design of the 2D model of the fusion target pill based on machine learning includes: parameterization of the fusion target pill, mesh division, construction of a database, data dimensionality reduction, construction of a neural network, and genetic algorithm optimization. On the basis of the original 2D model grid of the fusion target, the H-type grid is kept unchanged, only the O-type grid is replaced, and the total number of grid points is kept unchanged, so that the 2D model of the modified fusion target with any modeling parameter can quickly extract the physical parameters at the specified position in the flow field, with better geometrical adaptability, and improve the efficiency of data processing effectively. By combining the principal component analysis method and artificial neural network method to achieve the reconstruction of the flow field of the two-dimensional model of the fusion target, under the premise of guaranteeing the accuracy requirements, reducing the dimension and compression of the computational volume, thus reducing the time-consumption and saving the computational resources, realizing the rapid evaluation of the radiation hydrodynamic performance of the two-dimensional model of the fusion target and the rapid search for the optimization of the two-dimensional model of the fusion target, and providing a new idea for the high-efficient design of the two-dimensional model of the high-performance fusion target.

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Machine learning-based 2D model optimization design for fusion target pellets

Liu,

Wang,

Yuan

2024

J. Phys.: Conf. Ser.

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The new approach to writing source code for high-performance computing of Z-FFR models based on artificial intelligence and big data

Liu

Xiong

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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The Z-FFR (Z-Pinch driven fusion fission hybrid reactor) contains two physical processes, nuclear fusion and nuclear fission, and has a complex structure itself. Using artificial intelligence and big data technology to construct the digital Z-FFR, a decision decomposition method for writing the source code of the digital Z-FFR (Z-Pinch driven fusion fission hybrid reactor) by an artificial intelligence programmer is also proposed, including the following steps: using big data to build a normalized description of the digital Z-FFR; based on the normalized description of the digital Z-FFR, a dimensional decomposition method is used to split the digital Z-FFR modeling, digital Z-FFR simulation and digital Z-FFR writing structure to obtain the decision splitting set of digital Z-FFR. The decision selection method is determined according to the digital Z-FFR decision splitting set and the digital Z-FFR source code writing is completed. The decision splitting method for writing digital Z-FFR source code with artificial intelligence and big data proposed in this paper decomposes the writing logic of digital Z-FFR, and uses different artificial intelligence decision methods to complete the writing of digital Z-FFR source code according to different writing logics, which overcomes the disadvantages of long development cycle, repetitive development workload and high learning cost of various existing simulation systems.

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Artificial intelligence-assisted physical design of fusion materials

Liu,

Qu,

Yuan

et al. 2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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Inertial confinement fusion (ICF) is an approach to fusion that relies on the inertial of the fuel mass to provide confinement. Conditions under which inertial confinement is sufficient for efficient thermonuclear burn, a capsule (generally a spherical shell) containing different materials and thermonuclear fuel is compressed in an implosion process to conditions of high density and temperature. Another important process is the energy transport, in which the hohlraum coupling effect and hohlraum radiation uniform are the important physical parameters that can limit the energy transport. It is described the ignition condition by different physical parameters. Because the physical processes in fusion ignition are complex, and more physical quantities in the existence of multiple correlations and strong correlations, a single model often can not cope with fusion physics, this paper uses artificial intelligence, combined with complex physical processes, repeated model combination and iteration, to obtain the fusion materials model combination method, to provide an optimal parameter library for experimental physics. In this paper, we obtain the neutron yield of the main fuel DT can reach 10 20 , which indicates that the aim of achieving fusion can be achieved.

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Z-FFR reliability analysis by big data in collaboration with artificial intelligence and industrial software

Cited by 3 publications

References 6 publications

Machine learning-based 2D model optimization design for fusion target pellets

Machine learning-based 2D model optimization design for fusion target pellets

The new approach to writing source code for high-performance computing of Z-FFR models based on artificial intelligence and big data

Artificial intelligence-assisted physical design of fusion materials

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