直线加速器超导段故障补偿的人工智能算法研究

Research on Segmented Failure Compensation of Superconducting Section in Linear Accelerator by Artificial Intelligence Algorithm

强流直线加速器的长时间稳定运行是该领域的难点和前沿课题之一。以加速器驱动嬗变研究装置(CiADS)的加速器为例,利用TraceWin软件模拟的虚拟加速器数据,提出了基于人工智能算法的超导腔失效的分段故障补偿方法。当有腔体故障时,故障腔的相邻器件将用于重新匹配束流包络,而所有下游腔均可用于补偿束流能量。与传统的优化方法相比,该方法可实现低能耗的超导腔段的故障补偿,具有计算速度快、普适性强等优点,为实际应用器件故障补偿提供了新的可行性。

Long-term stable operation is one of difficult and frontier topics in the field of high-current linear accelerators.In this article,taking the China initiative Accelerator Driven System(Ci ADS)accelerator as an example,of whose virtual model implemented with Trace Win software by using simulation data,a segmented failure compensation method of superconducting(SC)cavity by artificial intelligence algorithm has been proposed.The neighboring elements of the failure cavities are used to rematch the beam envelope,while all the downstream cavities are used to compensate the beam energy.Compared with traditional optimization methods,this research can realize the failure compensation of low energy SC section,and has the advantages of fast calculation speed and strong versatility.It provides new feasibility for the practical application of component failure compensation.