Abstract
The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.
Highlights
The main rotor is the most significant component of any helicopter because it generates the needed lift supporting the weight of the entire rotorcraft and provides the control moments required for the execution of different maneuvers
The results showed that, when the adopted structural model was not accurate enough, the augmented Kalman filter (AKF) still provided a good estimation of the states at the expense of the loads’ accuracy
All the analyses presented in the paper refer to a quasi-steady aerodynamic model
Summary
The main rotor is the most significant component of any helicopter because it generates the needed lift supporting the weight of the entire rotorcraft and provides the control moments required for the execution of different maneuvers. Several methodologies have been proposed in the literature This problem can be approached in two related fields of research: structural component monitoring and prediction of structural behavior or aerodynamic loads. When a joint states/inputs estimation is performed, both EKF and UKF can be employed in a variant way In this context, two different approaches are presented in the literature: the dual Kalman filter (DKF) [26] and the augmented Kalman filter (AKF) [16]. The Kalman filtering technology has been widely applied in a lot of research, but the intent of the authors is to demonstrate its potential in the rotorcraft field for load prediction, thereby exploring the advantages in the model complexity and needed sensor data.
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