Research on the simulation environment of hard X-ray nanoprobe beamline station

Abstract In the hard X-ray nanoprobe beamline station, experiments need to be performed by adjusting the optical equipment in order to obtain good beamline performance. Due to many factors, only through the use of intelligent optimization, the beamline performance can be quickly improved. Therefore, an intelligent optimization method is proposed in this work to improve the performance of the beamline rapidly by using an adaptive algorithm to optimize the motor shaft of each device. Moreover, this paper introduces the simulation environment of the hard X-ray nanoprobe beamline equipment. In this environment, a multi-axis parallel optimization model of several optical devices is designed, and the ionization chamber feedback is replaced by Rastrigin function. Furthermore, the differential evolution algorithm is used to verify the model. The optimization tests of multiple devices in the beamline are carried out, and the automatic optimization of the devices is realized. As for the theoretical result, the designed intelligent beamline optimization program is capable of converging to the optimal value within 3-6 minutes on the simulation platform, this automated process could potentially enhance beamline adjusting efficiency by 10-20 times compared to manual beamline adjusting.