Clustering fusion algorithm for selection of historical beam steady-state transmission data in CAFe facility

Abstract

Accelerator-driven systems (ADS) are promising technologies for nuclear waste transmutation and energy production. The China ADS Front-end Superconducting Demo Linac (CAFe) is a prototype of the China Initiative Accelerator Driven System (CiADS), which aims to verify the feasibility of key technologies of CiADS. In this article, a novel method for historical data screening of the beam transport in the medium energy beam transport (MEBT) section of CAFe is presented. A clustering fusion algorithm based on unsupervised learning and beam transmission characteristics is designed to extract a large number of samples with the beam in steady-state transmission from historical beam data. A deep neural network model was constructed to fit the beam transport characteristics and verify the reliability of the screened data samples. The method can improve the efficiency and accuracy of data analysis and provide valuable insights for the optimization and control of beam transport in CAFe.

Ion beam stability prediction of ECR ion source based on TCN-DTW network

“…In the realm of accelerator archiving history data, researchers at the China ADS Front-end Superconducting Demo Linac (CAFe) facility devised a clustering fusion algorithm based on unsupervised learning and beam transmission characteristics. This algorithm filters and analyzes historical accelerator data, with an evaluation confirming its effectiveness [20]. Furthermore, in [21], the authors proposed an accelerator anomaly prediction model based on isolated forests, effectively reducing the false interlock phenomenon in the accelerator machine protection system caused by anomalies.…”