Zhao, H.; Meng, F.; Wang, Z.; Yin, X.; Meng, L.; Jia, J. Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning. Appl. Sci.2023, 13, 13115.
Zhao, H.; Meng, F.; Wang, Z.; Yin, X.; Meng, L.; Jia, J. Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning. Appl. Sci. 2023, 13, 13115.
Zhao, H.; Meng, F.; Wang, Z.; Yin, X.; Meng, L.; Jia, J. Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning. Appl. Sci.2023, 13, 13115.
Zhao, H.; Meng, F.; Wang, Z.; Yin, X.; Meng, L.; Jia, J. Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning. Appl. Sci. 2023, 13, 13115.
Abstract
Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.
Keywords
FP cavity; Multi-physics coupling; Finite element method; Data learning; Surrogate model; Evolutionary algorithm
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
