Modal analysis of propellant casing for solid rocket engines based on CEEMDAN

The structural design and manufacturing of spacecraft require meticulous attention to lightweight characteristics, rigidity, and stability, which are fundamental for the success of space missions. As a result, designs must be subjected to rigorous analys is, testing, and validation to ensure structural reliability and safety. However, the use of Empirical Mode Decomposition (EMD) methods for identifying dense modes presents challenges due to endpoint effects, which can compromise the accuracy of modal parameter identification. This study evaluates the performance of EMD and its enhanced variant, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), in identifying complex signals with closely spaced frequencies. The analysis reveals that CEEMDAN significantly outperforms EMD by effectively mitigating the endpoint effect through the application of Autoregressive (AR) extension techniques. Simulation experiments conducted on the propellant casing of solid rocket engines using ANSYS software demonstrate that CEEMDAN is particularly effective in handling low-frequency and widely dispersed signals, although it shows only moderate performance in decomposing high-frequency and concentrated signals. These findings offer crucial insights for spacecraft structural design and manufacturing, especially in addressing c omplex signal challenges and improving decomposition accuracy.