16:00   Data-driven testing Chair: Holger Mai 16:00 30 mins Aircraft vibration from React to Predict Hoang Thien Phu VO, Völker Johannes Abstract: Aircraft vibration is a natural occurrence due to various sources such as engine operation, aerodynamic forces, system abnormality and mechanical components. Today, aircraft vibration is a ‘burden’ for airlines due to its cost & long troubleshooting. This article will present the Data Based process for identifying, continuously monitoring vibration source(s) and then predicting the coming vibration event(s) , so called Predictive Maintenance for Vibration. Within this process, existing aircraft sensors (low quality and low sampling rate) will be processed and compared with a vibration database, flight by flight, for detecting any abnormal vibration. The vibration database has been built based on several ground/flight dynamic tests and validated aeroelastics models. 16:30 30 mins A Calibration Method for Camera-Aided Inertial Estimation of Wing Shape Leandro Lustosa Abstract: Previous work [1] develops a real-time wing deformation estimation technique for Very Flexible Aircraft (VFA) stabilization and control. In particular, this capability supports the design of novel Stabilization Augmentation System (SAS) architectures for VFA that exhibit structural dynamic modes (e.g., first bending, twisting) coupling with traditional flight dynamics modes (e.g., short period, phugoid) and therefore require real-time measurements of structural deformation, on top of the traditional rigid-body flight mechanics estimates, for simultaneous active stabilization of rigid and flexible states. The method [1] uses an array of rate-gyros linearly installed along the wingspan and a single sighting device on the wing root that tracks a small number of lumped visual markers on fixed stations on the wing surface. While the estimation errors in standalone rate-gyro angular rates integration would diverge with time and the measurement availability bandwidth in standalone embedded computer vision tracking systems is low (when compared to typical attitude controller bandwidths), the previously proposed Extended Kalman Filter (EKF)-based fusion estimation algorithm yields in simulation high-bandwidth estimates with bounded error covariances in time. Additionally, the technique does not rely on structural dynamic models and thus can be more easily adapted from one aircraft to another. The current paper contributes to this effort by examining its hardware implementation challenges. In particular, the EKF requires accurate knowledge of the camera configuration (i.e., its position, tilt angle, and focal length). We show that ruler-based and camera resectioning measurement values yield poor results, are challenging to fine-tune by hand, and ultimately call for more sophisticated techniques. The present work proposes an optimization-based method for calibrating the critical parameters (i.e., intrinsic and extrinsic camera parameters) through the help of a motion capture system. It also studies its observability characteristics under different sets of free parameters to tune. The resulting fine-tuned EKF shows significantly reduced estimation errors over manual calibration and sets the groundwork for its use in uncertain real-world conditions. REFERENCES [1] Lustosa, L. R., Kolmanovsky, I., Cesnik, C. E. S., and Vetrano, F. (2021). Aided inertial estimation of wing shape. Journal of Guidance, Control, and Dynamics, 44(2), 210-219. 17:00 30 mins Estimation of slender body elastic rates and accelerations using a combination of measured data Ilya Genkin, Daniella E. Raveh Abstract: The paper presents a methodology to estimate the in-flight elastic and rigid-body dynamics of a slender flexible body using multiple data and an approximate aeroelastic model. It is intended to overcome the inherent shortcoming of flight control systems that typically rely on a rigid-body model whereas the measured response includes both the rigid-body dynamics and the elastic dynamic response. The study presents a Kalman state estimation approach based on an approximated aeroelastic model of the vehicle dynamics and a combination of measured data from different sensors, to estimate structural modal deformations, rates, and accelerations. The estimated states can then be used to reconstruct the elastic response at the IMU location, from which the rigid body dynamics can be computed. The paper presents the test case of a simple slender-body vehicle under random time-varying loading. Numerical results show that the proposed methodology accurately reconstructs the elastic dynamic elastic response. A parametric study presents the effect of using different sensor data of various noise levels. 17:30 30 mins Prediction of Limit Cycle Oscillations Based on Dynamic Eigen Decomposition of Flight Test Data TAEHYOUN KIM Abstract: During the design and test phases of aircraft structure, flutter boundaries are computed using analysis tools such as the p-k iterations or eigenvalue analysis. Also, for the purpose of certification flight flutter test (FFT) is conducted to estimate the critical aeroelastic damping and predict the onset of flutter experimentally. However, from the practical perspective Limit Cycle Oscillation (LCO) which is referred to as an aeroelastic vibration with a finite amplitude is more critical because it represents not only the true nonlinear nature of the fluid-structure interaction but also the potentially more dangerous dynamic instability that could occur at speeds lower than the flutter point. Previously, based on the concept of the Dynamic Eigen Decomposition (DED) and a frequency domain stability theorem, a new theory of flutter prediction was developed [1] and modified for applications to FFT with limited actuators and sensors [2]. In the present work, the original techniques are extended and modified to account for the large amplitude LCOs. For this initial work, we will focus on the structural nonlinearity caused by the control surface free-play, assuming that aerodynamically the system remains dynamically linear. It will be shown that when a nonlinear solution is sought in a simple harmonic fashion similar to harmonic balance approach to general engineering problems, the LCO can be interpreted as a dynamic instability with zero effective damping. Thus, as in the linear flutter prediction an LCO can be found by checking the dynamic eigenvalues λ(ω)’s in the frequency domain with an effective control surface stiffness. First, harmonic excitations of the control surface are carried out at discrete frequencies at several subcritical flight conditions. Next, using the data a new DED is formulated combining the two oscillation cases in two parameters, i.e., the variable dynamic pressure Δq and variable stiffness of the control surface hinge ΔK: c_c [-ω^2 M+jωC+K+mΔK-(q_D+kΔq)Q(k)]^(-1) mb_c=v_t (k,ω) λ_t (k,m,ω) w_t (k,ω) where λ_t (k,m,ω)=m〖λ^'〗_t (k,ω)/(1-m〖λ^'〗_t (k,ω)) and 〖λ'〗_t (k,ω)’s are dynamic eigenvalues when only the dynamic pressure changes. With the new formulation one can iterate on k and m, i.e., dynamic pressure and the control surface amplitude until a LCO point is found. This procedure is very similar to the procedure of linear flutter prediction except that we now have two, instead of one, parameters. The essential characteristic of the nonlinear aeroelastic LCO phenomena that there exists one-to-one relation between the amplitude of the oscillation and dynamic pressure is well captured. Most importantly, only a single excitation and a single sensor are required, and this is very desirable for LCO prediction based on experimental data from wind tunnel or flight tests. Furthermore, no extra flight tests are necessary beyond the regular flutter tests. For the upcoming IFASD paper, the proposed scheme will be demonstrated using computational simulations of a tapered straight wing with four flaps along the trailing edge. Should FFT data be available, LCO prediction results based on the experimental data will be also included and reported. References [1] Kim, T., Flutter Prediction Methodology Based on Dynamic Eigen Decomposition and Frequency-Domain Stability, Journal of Fluids and Structures 2019; 86; 0-13. [2] Kim, T., Progressive Flutter Prediction Using Flight Data with Limited Sensors and Actuators, SciTech, January 8-12, 2024, Orlando, FL, USA.

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