State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach

Area of Science:

• Aerospace Engineering
• Computational Fluid Dynamics
• Vibrations and Acoustics

Background:

• Vibratory loads on helicopter main rotors stem from blade-fluid interactions in non-axial flow.
• Predicting aerodynamic loads and component dynamics is crucial for preventing rotorcraft failures.
• Existing methods often require extensive sensor data or high-fidelity models for accuracy.

Purpose of the Study:

• To apply the Kalman filtering technique for accurate rotor load estimation in helicopters.
• To demonstrate the filter's ability to work with a minimal sensor set.
• To compensate for uncertainties in low-fidelity models using sensor and model error accounting.

Main Methods:

• Kalman filtering technique applied to rotor load estimation.
• Utilized a coupled multibody-aerodynamic model.
• Investigated two distinct sources of uncertainty in the model.

Main Results:

• Accurate state reconstruction achieved with a limited sensor layout.
• Efficient estimation of aerodynamic loads on rotating blades.
• Demonstrated the Kalman filter's effectiveness in handling model and sensor uncertainties.

Conclusions:

• Kalman filtering provides an efficient method for helicopter rotor load and state estimation.
• The technique allows for accurate predictions even with limited sensor data and low-fidelity models.
• This approach offers a viable strategy for enhancing rotorcraft safety and performance prediction.