Artificial intelligence-assisted design of load-driver end docking in Z-FFR fusion explosion chambers

Wenbin Xiong; Zhangchun Tang; Pan Liu; Hongwei Qiao; Fanyu Qu; Qiang Gao

doi:10.1117/12.3007174

25 October 2023 Artificial intelligence-assisted design of load-driver end docking in Z-FFR fusion explosion chambers

Wenbin Xiong, Zhangchun Tang, Pan Liu, Hongwei Qiao, Fanyu Qu, Qiang Gao

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 128015A (2023) https://doi.org/10.1117/12.3007174
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

The Z-FFR(Z-pinch driven fusion fission hybrid reactor) is composed of a pulsed power driver, a fusion target and a subcritical reactor. The docking of the fusion load in the burst chamber is the most critical link to shorten the fusion cycle and ensure the quality of fusion. The electrical and mechanical properties of the load need to be ensured during the docking process, and the difficulty of docking quality assurance lies in the high precision invisible docking and dynamic operation. Therefore, in this paper, we first conduct docking experiments in the invisible case using a multi-degree-offreedom parallel platform to obtain the initial data set for docking. The training data set is used to train the neural network model and the prediction data set is optimised by the Wolf Pack algorithm for deep learning models to improve the accuracy of docking prediction points. The mathematical model of the soft control enables the docking of fusion loads without visual guidance and still with the assistance of multidimensional force sensors only, with high quality and efficiency. In general, the motion state and docking mode of the fusion load are planned by the artificial intelligence assisted training. The docking of the fusion load to the end of the drive is accomplished in a vision-free operation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenbin Xiong, Zhangchun Tang, Pan Liu, Hongwei Qiao, Fanyu Qu, and Qiang Gao "Artificial intelligence-assisted design of load-driver end docking in Z-FFR fusion explosion chambers", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 128015A (25 October 2023); https://doi.org/10.1117/12.3007174

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KEYWORDS

Data modeling

Design and modelling

Mathematical modeling

Neural networks

Vacuum chambers

Mathematical optimization

Evolutionary algorithms

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