Estimation of a Craig–Bampton equivalent model using a hybrid particle swarm optimization for DCLA purposes.

It is common practice in industry to deliver a Craig–Bampton reduced version of the spacecraft's finite element model to the launch authorities to perform the dynamic coupled load analyses. During the dynamic coupled load analyses, the finite element model of the spacecraft is coupled with the finite element model of the launcher to predict the responses caused by the vibration environment produced during the launch phases (e.g., lift-off, engine start-up, etc.). Before being Craig–Bampton-reduced, the finite element model needs to be correlated and validated against physical test results to guarantee its capability to reproduce the actual behaviour of the physical hardware, which is essential to perform a meaningful dynamic coupled load analyses. Given the complexity and size of the finite element model, the entire process can be very demanding. In this paper, an alternative to the mathematical models usually used in the dynamic coupled load analyses is investigated. The main idea is to synthesize a Craig–Bampton equivalent model directly from the response data of a vibration test experiment. The system identification problem is solved using a particle swarm optimization algorithm, tailored specifically for this type of application, the hybrid PSO and Local Search (hPSO-LS). A mutation operator from genetic algorithm is used to improve the solution and the local search method to refine the final result. The proposed method is numerically tested on a 5-degrees of freedom lumped mass system, with full stiffness and damping matrices. A sensitivity analysis of the search space size is also carried out to investigate its influence on the final results. The hPSO-LS is tested in both noise-free and noisy scenarios. • Hybrid variant of PSO with refined research is proposed to solve system identification problem. • Reconstruction of full stiffness and damping matrices of a lumped mass system from accelerations only. • Sensitivity analysis on the size of the search space is carried out. [ABSTRACT FROM AUTHOR]