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

Gaoyang Liu; Pan Liu; Wenbin Xiong; Zilong Yuan; Zhangchun Tang; Yan Shi; Hongwei Qiao; Chencheng Liu; Qiang Gao

doi:10.1117/12.2684714

21 July 2023 The new approach to writing source code for high-performance computing of Z-FFR models based on artificial intelligence and big data

Gaoyang Liu, Pan Liu, Wenbin Xiong, Zilong Yuan, Zhangchun Tang, Yan Shi, Hongwei Qiao, Chencheng Liu, Qiang Gao

Author Affiliations +

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171W (2023) https://doi.org/10.1117/12.2684714
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

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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Gaoyang Liu, Pan Liu, Wenbin Xiong, Zilong Yuan, Zhangchun Tang, Yan Shi, Hongwei Qiao, Chencheng Liu, and Qiang Gao "The new approach to writing source code for high-performance computing of Z-FFR models based on artificial intelligence and big data", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171W (21 July 2023); https://doi.org/10.1117/12.2684714

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KEYWORDS

Artificial intelligence

Data modeling

Computer simulations

Design and modelling

Computing systems

Control systems

Machine learning

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