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Aeroelastic testing 4
Chair: Johannes Meijer
16:00
30 mins
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T-tail transonic flutter wind tunnel test part 1: Sealing system design and model testing
Valentin Lanari, Arnaud Lepage, Elsa Bréus
Abstract: This paper presents the test setup improvements and the experimental results of the recent T-tail transonic flutter test performed at ONERA S2MA pressurized wind tunnel in November 2022. The test campaign is the culmination of flutter investigations initiated in Clean Sky 1's “Smart Fixed Wing Aircraft Integrated Technology Demonstrator” program where U-tail configurations were studied [1]. The current presented work was performed in the frame of Clean Sky 2's Airframe ITD program in partnership with Dassault Aviation.
First, the paper presents the sealing system developed to improve the test setup and tackle issues encountered in the previous 2016 test campaign. Air leakage from the fuselage at the root of the tail wing model led to unwanted aerodynamic effects, and shifting flutter onset towards higher critical pressure. Efforts were made to develop a sealing system solution involving labyrinth sealing and fine gap tuning during dynamic displacements of the test setup while keeping a healthy model dynamic behavior. The heavily instrumented model allowed validation of the sealing solution during the wind tunnel tests without any negative impact on the aeroelastic characteristics of the T-tail model.
Then, the wind tunnel test and its associated experimental results and observations over four geometrical configurations of T-tail model are presented, with variations of yaw and dihedral angle of the horizontal stabilizer. Both steady and unsteady aerodynamics were investigated, including Mach number variations (from M=0.7 to M=0.925), forced pitch motion excitation frequency, angle of attack, and air pressure variations. The remotely configurable test setup and safety system allowed a controlled investigation of aeroelastic instabilities apparition beyond flutter onset. The extensive database measured helped understanding aeroelastic instabilities occurring on a T-tail model, and permitted to confront our numerical capabilities to predict flutter instabilities in transonic regimes. The numerical restitutions using high-fidelity CFD tools are presented in a companion paper [2].
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16:30
30 mins
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T-tail transonic flutter wind tunnel test. Part 2: Numerical restitution
Sylvie Dequand, Valentin Lanari
Abstract: The paper presents the main results of the numerical restitution of the test campaign realized on a T-tail flutter model in subsonic and up to high transonic domains. The Wind Tunnel Tests were carried out in ONERA S2MA pressurized wind tunnel at the end of 2022 in the framework of the Clean Sky 2's Airframe ITD program and will be presented in a companion paper [1].
Numerical results obtained with high-fidelity fluid-structure coupling simulations performed using the elsA CFD solver (ONERA-Safran property) [2] are compared to wind-tunnel test data and to low-fidelity numerical results.
Four configurations of T-tail were measured during the test campaign, in order to investigate the effect of yaw angle and dihedral on the flutter phenomenon. These different geometries enable also to assess the ability of our numerical tools to predict corner flow aerodynamic phenomena arising in the area of the T-tail surface intersections.
A good correlation is obtained between numerical and experimental steady pressure coefficients, even at higher Mach numbers. For the unsteady pressure coefficients, the aerodynamic responses are computed for a forced motion applied to the T-tail model and the effects of different excitation parameters are assessed.
Aeroelastic stability of a T-tail configuration is also investigated and coupled high-fidelity simulations are capable to predict the good evolution of the critical pressure with Mach number. Further work is still in progress and numerical and experimental results will be compared for the other T-tail flutter models.
ACKNOWLEDGEMENTS
This work has been funded within the frame of the Joint Technology Initiative JTI Clean Sky 2, AIRFRAME Integrated Technology Demonstrator platform "AIRFRAME ITD" (contract N. CS2-GAM-AIR-2020-945521) being part of the Horizon 2020 research and Innovation framework program of the European Commission.
REFERENCES
[1] Lanari V., Lepage A., Breus E., “T-tail transsonic flutter wind tunnel test – Part 1: Seal-ing system design and model testing”, IFASD 2024, Den Haag.
[2] Cambier, L., Heib, S., Plot, S., “The ONERA elsA CFD software : input from research and feedback from industry”, Mechanics & Industry, 14(3): 159-174, 2013.
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17:00
30 mins
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Flutter prediction correlations with wind tunnel measurements on a T-tail flutter mock-up
Elsa Bréus, Nicolas Forestier, Zdenek Johan, Eric Garrigues
Abstract: Flutter computations on a T-tail aircraft hold challenges as flutter behavior is significantly driven by specific aerodynamic phenomena. Interactions between tail surfaces have to be computed correctly to predict flutter accurately. Developments were performed at DASSAULT-AVIATION to improve in-house solvers for these specific configurations. To validate numerical tools, some reference experimental data are required. To do so a wind tunnel test mock-up has been designed and manufactured in the frame of CleanSky2 project in cooperation between DASSAULT-AVIATION, ONERA and RUAG. This mock-up was tested in 2016 for U-tail configurations. A second wind tunnel test campaign took place in 2022 in ONERA S2MA pressurized wind tunnel.
The second campaign allowed testing different T-tail configurations and gathering data for subsonic, transonic and high transonic domains. Flutter curves have been measured up to the flutter point thanks to an efficient safety system that allowed reaching flutter boundary numerous times without structural damage. The tests, conducted up to Mach 0.925, have shown good repeatability leading to a high confidence in the measures. To help numerical restitution the model was extensively instrumented with pressure sensors, accelerometers and strain gauges. The mock-up dynamic behavior was measured through ground vibration tests, allowing the tuning of the Finite Element Model of the mock-up.
Configurations tested consisted in several incidence settings of the horizontal tail plane to cover various lift forces and a dihedral effect. Both effects are of prime importance when computing flutter. Thus the need for validation of the numerical predictions for those effects. This paper presents correlations between experimental data and numerical computations. The methods used by DASSAULT-AVIATION for the numerical restitution are linearized Navier-Stokes CFD and enhanced Doublet Lattice Method - taking into account rolling, yawing and spanwise in-plane motion in addition to the regular pitching and plunging motions. Good correlations are obtained as depicted in Figure 2. This article shows that DASSAULT-AVIATION tools allow an accurate prediction of flutter behavior thanks to accounting for all the particular phenomena of T-tail configurations.
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17:30
30 mins
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Feedforward backpropagation artificial neural networks applied to dynamic loads of a military transport aircraft
Gabriel Buendia, Beatriz Pulido, MIguel Torralba, Manuel Reyes, Felix Arevalo, Hector Climent
Abstract: Artificial neural networks are known for solving complex problems and detecting nonlinear relationships between variables in a fast and accurate manner. Moreover, their storage memory optimization makes them an attractive tool for aeronautical applications such as control algorithms, component health monitoring and simulators [1]. In order to study the feasibility of applying feedforward backpropagation networks to dynamic loads problems, two applications on a military transport aircraft have been analysed:
1. Potential landing overload events assessment, where classification neural networks were used.
2. Fatigue turbulence loads prediction, where regression neural networks were used.
The following methods have been explored for the proper development of these neural networks:
1. Exploration of the database inputs and outputs. This includes an initial assessment of the relevance of the input parameters, the selection of the relevant outputs to be monitored and the identification of the most dense regions in the database.
2. Neural training and hyperparameter tuning using Keras/TensorFlow 2.0. Sensitivity studies are performed to select the combination of the parameters that specify the details of the learning process which provide the best results, either for predicting a single or multiple outputs simultaneously. The definition of the cost function and metrics is of special relevance.
3. Interpolation and extrapolation capabilities assessment. Evaluations on the ability of the trained networks to predict results from non-trained inputs, which are inside or outside the limits of the variable space in which they have been trained, were performed.
4. Performance evaluation with recorded flight data. Influence of the errors coming from conservative estimations of the neural networks on the fleet operations is evaluated.
The extension of the methodology to similar dynamic loads problems such as fatigue discrete gust and taxi loads, as well as any other overload events are planned to be explored in the future.
References:
[1] Faller, W. E. & Schreck, S. J.: Neural networks: Applications and opportunities in aeronautics. Progress in Aerospace Sciences 32.5, 433-456 (1996)
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