TY - GEN
T1 - Dynamic Shape Sensing of the A3TB Wind Tunnel Model Using Fiber Optics Strain Data and the Kalman State Estimator
AU - Joels, Tsoof
AU - Raveh, Daniella E.
N1 - Publisher Copyright: © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The study presents an application of a recently proposed Kalman state estimator-based strain-to-displacement methodology for estimation of strains and displacements in a wind tunnel test of a flexible wing configuration based on strain data measured in optical fibers. Strains and wing tip displacements were estimated from measured strain data and compared to estimations from a least-squares strain-to-displacement process, to simulated strains from an aero servo elastic model, and to experimentally measured strains and displacements. As both the Kalman state estimator and the least-squares process rely on a set of strain modes, the study focused on the impact of using different strain mode sets on the estimation. Specifically, one strain mode set was computed in a finite-element free vibration analysis and the other was extracted from a ground vibration test, based on strain data recorded in response to a hammer hit, analyzed via spectral proper-orthogonal decomposition. The Kalman state estimator also relies on an aero servo elastic model of the system, which is computed per airspeed. The study examined how different aero servo elastic models, generated for airspeeds different than the nominal (test) speed, affect the accuracy of the estimation. Our results show that when good-quality, continuous strain data is available, using either set of strain modes results in accurate prediction of strains in unmeasured locations and of the displacements. Airspeed mismatch in the ASE model was overcome by accounting for its effects as process noise. Accurate strain predictions were obtained based on ASE computed for airspeed approximately 15% higher or lower than the actual wind tunnel airspeed. The KSE offers a reliable, robust means for strain-to-displacement transformation as it is less affected by measurement and modeling errors, as shown in this study.
AB - The study presents an application of a recently proposed Kalman state estimator-based strain-to-displacement methodology for estimation of strains and displacements in a wind tunnel test of a flexible wing configuration based on strain data measured in optical fibers. Strains and wing tip displacements were estimated from measured strain data and compared to estimations from a least-squares strain-to-displacement process, to simulated strains from an aero servo elastic model, and to experimentally measured strains and displacements. As both the Kalman state estimator and the least-squares process rely on a set of strain modes, the study focused on the impact of using different strain mode sets on the estimation. Specifically, one strain mode set was computed in a finite-element free vibration analysis and the other was extracted from a ground vibration test, based on strain data recorded in response to a hammer hit, analyzed via spectral proper-orthogonal decomposition. The Kalman state estimator also relies on an aero servo elastic model of the system, which is computed per airspeed. The study examined how different aero servo elastic models, generated for airspeeds different than the nominal (test) speed, affect the accuracy of the estimation. Our results show that when good-quality, continuous strain data is available, using either set of strain modes results in accurate prediction of strains in unmeasured locations and of the displacements. Airspeed mismatch in the ASE model was overcome by accounting for its effects as process noise. Accurate strain predictions were obtained based on ASE computed for airspeed approximately 15% higher or lower than the actual wind tunnel airspeed. The KSE offers a reliable, robust means for strain-to-displacement transformation as it is less affected by measurement and modeling errors, as shown in this study.
UR - http://www.scopus.com/inward/record.url?scp=85199490781&partnerID=8YFLogxK
U2 - https://doi.org/10.2514/6.2023-1310
DO - https://doi.org/10.2514/6.2023-1310
M3 - منشور من مؤتمر
SN - 9781624106996
T3 - AIAA SciTech Forum and Exposition, 2023
BT - AIAA SciTech Forum and Exposition, 2023
T2 - AIAA SciTech Forum and Exposition, 2023
Y2 - 23 January 2023 through 27 January 2023
ER -