The beamline optical component structural parameter optimization design based on PyAnsys and deep learning integration

Abstract

With the rapid development of high-repetition rate X-ray free electron laser (FEL), the issues of thermal deformation and stress in optical elements, such as mirrors, under high thermal loads have become increasingly prominent. Traditional design optimization relies on repeated Thermal-Structural coupling finite element simulations, which face bottlenecks, including low optimization efficiency and insufficient utilization of historical data. This paper proposes an optimization approach that combines PyAnsys parametric scanning with deep learning-based surface shape prediction, aiming to enhance the optimization efficiency of mirror cooling structures and support parameters. A parametric simulation platform is constructed using PyAnsys Geometry and PyMechanical, enabling the rapid generation of model variants within 30 s. Additionally, a historical database containing geometric features, physical parameters, and operating conditions is established. Furthermore, a deep neural network-based mirror height error prediction model is developed, and experiments show that the model can predict surface height errors with RMS <0.08 nm in water-cooled silicon mirror cases. This method has the potential to shorten the traditional design iteration cycle to a few hours, significantly enhancing data reuse. Future work should focus on incorporating incremental learning and physical constraints to improve the reliability of extrapolation for multi-condition scenarios and refine the optimization loop.