The numerical model of a staggered labyrinth seal working with supercritical carbon dioxide (SCO₂) is established. The dynamic and static characteristics of the staggered labyrinth seal for different axial shifting distances of the rotor, various cavity geometries (heights/widths of the rotor convex plate, heights of the seal cavity), and seal clearances were investigated and compared with the conventional see-through labyrinth seal. The results show that the effective damping coefficient (C_eff) with positive axial shifting distance is higher than that with negative axial shifting distance. When the rotor with convex plate operates without axial shifting, the cross-coupled complex dynamic stiffness (h_R) of the staggered labyrinth seal shows little effect on the C_eff, and the average direct damping (C_avg) has a dominant influence on the C_eff. As the whirling frequency (Ω) is lower than 60Hz, the C_eff decreases with increasing height of the rotor convex plate. For Ω<140Hz, the damping coefficient generally increases with the decreasing height of the seal cavity. For Ω<160Hz, the C_eff of the see-through labyrinth seal is about 107649 of the staggered labyrinth seal. Otherwise, the C_eff of the staggered labyrinth seal is about 105113 of the see-through labyrinth seal. The C_eff of the seal with the rotor convex plate width of 5.13mm is relatively high, which is conducive to the stability of the system. The C_eff increases with the decreasing seal clearance. The C_eff of the seal with 0.4mm clearance is about 116148 the seal with 0.6mm. The leakage flow rate of the staggered labyrinth seal of the see-through labyrinth seal is increased by about 45.5. The leakage flow rate of the staggered labyrinth seal decreases with the increasing convex plate height, the seal cavity height, and the decreasing seal clearance.

Wake and output power prediction of wind turbine is critical for the wind farm layout optimization. Previous studies used analytical wake models and computational fluid dynamics (CFD) methods to fulfill this prediction. However, these methods either exhibit inadequate prediction accuracy or need excessive computational demands during prediction processes. Thus, a novel wind farm prediction system is established using the full-model CFD simulation and a surrogate modeling method based on convolutional neural networks and generative adversarial networks to ensure the fidelity and efficiency of the prediction. By containing an incoming speed distribution generator module (Gin), a wake distribution generator model (Gw), a rotational speed prediction module (R), and a power prediction module (P), this system can predict high-dimensional incoming data, the rotational speed, power output, and three-dimensional wake fields. The system uses the incoming wind speed (Vin) and turbulence intensity to determine the optimal placement of turbines in the wind field. The Gin, R, Gw, and P module are validated through high-resolution experimental and computational data. The system is applied to a tandem wind farm and the Horns Rev 1 wind farm. The predicted data show satisfactory agreement with high-resolution experimental and computational data, fully validating the robustness and generalization capability of the system. The proposed prediction system is of great engineering significance for optimizing wind farm layout.