Monitoring the operating status of steam turbines is critical for efficient and safe power generation. This study proposes a digital twin design and modeling method for steam turbines, integrating reverse modeling techniques with genetic algorithm optimization. Firstly, the geometric design model is reconstructed from 3D-scanned point cloud data using point cloud fusion and surface reconstruction technologies, covering 13 stages of high-pressure rotor blades, 9 stages of intermediate-pressure stator blades, and the cylinder. Secondly, a physics-based model suitable for computational fluid dynamics simulations is generated using optimized mesh design parameters. To address unmeasurable parameters, a genetic algorithm is applied for data-driven design optimization, enhancing the dynamic simulation accuracy to 95%. Finally, leveraging a reduced-order model, real-time mapping of key physical fields is achieved. The effectiveness of the design methodology is validated under typical deep peak-shaving operating conditions.
Han et al. (Wed,) studied this question.