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Open AccessArticle

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

1
Siemens Digital Industries Software, Interleuvenlaan 68, 3001 Leuven, Belgium
2
KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300 B, 3001 Heverlee, Belgium
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DMMS-D Core Lab, Flanders Make, 3001 Leuven, Belgium
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Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano, Via la Masa 34, 20156 Milano, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4196; https://doi.org/10.3390/s20154196
Received: 25 June 2020 / Revised: 24 July 2020 / Accepted: 25 July 2020 / Published: 28 July 2020
(This article belongs to the Special Issue Shape Sensing)
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. View Full-Text
Keywords: state and load estimation; inverse identification; Kalman filter; multibody modeling state and load estimation; inverse identification; Kalman filter; multibody modeling
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MDPI and ACS Style

Cumbo, R.; Tamarozzi, T.; Jiranek, P.; Desmet, W.; Masarati, P. State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach. Sensors 2020, 20, 4196.

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