In order to realize the most economical operation of helicopter power system, the optimal rotor speed control method is proposed and designed. Firstly, the simplified performance calculation model of helicopter required power and turboshaft engine are established, which constitute the performance calculation model of helicopter power system together. Then, according to the above model, through applying blade loading constraint and selecting the minimum engine fuel flow as the optimization objective, an integrated optimization method of optimal rotor speed based on golden section method is proposed, which reveals the variation law of optimal rotor speed under different operation conditions. Finally, the numerical simulation and hardware-in-loop (HIL) simulation are conducted separately to validate the optimal rotor speed control method. The results indicate that compared with constant rotor speed operation, optimal rotor speed control method can decrease engine fuel flow by more than 21 %, which proves the significant effectiveness and satisfactory feasibility. The optimal rotor speed control method ensures that the helicopter power system operates at the optimal rotor speed, and is beneficial to reach the most economical cruise target.

In the digital twin modeling of aero-engine turbine blades, traditional CFD requires extensive computational time. Existing data-driven models also struggle to achieve multi-physics decoupling and high-accuracy predictions with extremely small sample constraints. To overcome these challenges, a multitask generative adversarial network (M-GAN) is proposed for multi-physics digital twin modeling. By incorporating a multi-branch attention mechanism into the U-Net generator, this method achieves the decoupling and prediction of multi-physics fields. Additionally, a multi-objective adaptive loss optimization strategy based on uncertainty weighting is proposed to mitigate gradient interference and mode collapse during the joint training of multiple coupled physical fields. Based on the proposed M-GAN, a high-performance digital twin model for turbine blades is developed using only 10 training samples, achieving accurate predictions of pressure, temperature, and airflow angle fields. The test results demonstrate that, on a test set comprising 90 different operating conditions, the average prediction error across all physical fields is only 1.85%, indicating strong cross-physics prediction capability, generalization, and robustness. This approach provides a solid algorithmic foundation for real-time, high-precision mapping in turbine blade digital twin systems.