A diffusion model with key node feature guidance for digital twin of rocket engine frames under variable working conditions

The structural health of the rocket engine frame (REF) that transmits engine thrust directly affects rocket performance. Therefore, it is urgent to develop structural health monitoring (SHM) technology for REF. Digital twin (DT) technology provides a real-time representation of the physical system's state and has gained significant attention. However, conventional DT methods combine physical properties with digital models under predefined working conditions. These methods often fail to perform effectively when working conditions vary. This study proposes a digital twin of rocket engine frames under variable working conditions via a diffusion model with key node feature guidance. In the offline phase, low-fidelity (LF) static strength characteristics of key nodes are embedded and cascaded across different conditions. The diffusion model’s hierarchical data processing enables the extraction of potential common features, making it possible to predict static strength distributions under any given scenario. In the online phase, high-fidelity (HF) data is introduced to bridge the gap between different fidelity distributions. The model is guided by the maximum mean discrepancy (MMD) between key node features of different fidelities to ensure effective feature fusion. Experiments with REF static strength data under various conditions validate the method’s effectiveness, demonstrating strong performance across multiple metrics. The implementation code is publicly available at https://github.com/wwb132559/denoising_diffusion_pytorch .