Speaker
Description
The Hefei Advanced Light Source (HALF) facility requires a high-precision tunnel control network to support accelerator alignment, equipment installation, and long-term geometric stability. This study focuses on the design and data processing optimization of particle accelerator tunnel control networks. An automated simulation workflow based on laser tracker measurement planning is developed to generate control network layouts, organize observation schemes, and perform batch Monte Carlo evaluation. The influence of network configuration is assessed using deviations from nominal coordinates and uncertainty indicators. To improve the reliability of tunnel control results, heterogeneous observations from laser trackers, digital levels, distance constraints, and close-range photogrammetry are further considered within a unified adjustment framework. In addition, time-series deformation prediction of control network points is explored using long-term remeasurement data and machine learning models. The proposed workflow provides a reproducible and transferable strategy for improving the precision, efficiency, and reliability of control network design and data processing for the HALF facility and similar large-scale accelerator projects.
