March 17, 2026

Towards an aircraft nervous system: Wind tunnel tests to evaluate sensor-model fusion technologies

Modern aircraft are equipped with a variety of different sensors to ensure functionality and operational safety. This sensor information needs to be processed in real time using intelligent algorithms for data fusion and machine learning. It allows flight conditions to be continuously monitored and intelligent control methods to be used in order to optimize operational performance. This may also be referred to as the establishment of an "aircraft nervous system," which represents an important step toward achieving current goals in aviation: reducing environmental emissions, enhancing operational safety, and increasing automation. To unlock the use of such methods in aircraft, these technologies are being tested and evaluated in dedicated wind tunnel experiments, in this case using a small-scale aeroelastic wing model [1]. As an example of such wind tunnel investigations, the test hardware and measurement setup employed within the impulse project "Sensor and AI Fusion for Enhanced PeRformance and Reliability" (SAFER²) are described.

A multitude of sensors and actuators: the Aeroelastic Wing Demonstrator

In order to investigate complex monitoring and control technologies, a hardware is required that includes both a wealth of sensors and control surfaces. Such hardware was developed in SAFER² in form of the aeroelastic wing demonstrator.

The aeroelastic demonstrator is a scaled-down model of the wing of a long-range wide-body aircraft with a high aspect ratio (see Fig. 1). Such aircraft have particularly long and slender wings, which reduce drag and thus fuel consumption. The aerodynamic design is based on the wing used in DLR's internal predecessor project oLAF ("optimal load-adaptive aircraft"), thereby allowing new insights to be directly compared to existing findings. The structural design has also been largely adopted and is based on a NASTRAN finite element model generated using the DLR-AE internal parametric modeling process ModGen (see Figs. 2 and 3) [2]. Yet, the demonstrator differs from its predecessor by being equipped with four, instead of five, controllable flaps, corresponding maintenance compartments for their actuators, as well as an increased number of sensors. The latter make it possible to examine the behavior of the wing during operation even more precisely and to test new concepts for load adaptation.

Fig. 1: Long-range wide-body aircraft as reference configuration
Image: 1/3, Credit:
Fig. 2: Structural model of the aeroelastic wing demonstrator
Image: 2/3, Credit:
Fig. 3: Lay-up design
Lay-up design of the glass fiber reinforced plastic (left). Stress analysis of the glass fiber layers in the fiber direction (right).
Image: 3/3, Credit:

The sensors are integrated within the wing body, on the wing skin, and at the wing root (see Fig. 4). The model itself contains 30 unsteady pressure sensors that can measure dynamic pressure changes on the wing surface. In addition, the wing features 10 acceleration sensors for measuring vibrations and oscillations, which are essential for determining the wing’s structural properties and dynamic aeroelastic behavior. Dynamic deformations can also be recorded using a marker tracking method (see orange markers in Fig. 4). In steady conditions, wing deformation can also be determined using optical fibers that run along the wingspan. In order to deflect the control surfaces accurately, their positions are captured by hall sensors that are installed on the flap hinges. Besides the "local" measurement information mentioned above, the entire wing model is mounted on a special measuring device – a so-called "piezoelectric balance". This piece of equipment allows us to observe not only what is happening locally at specific points, but also how the wing behaves as a whole. This information can then be used to specifically control the wing.

Fig. 4: Aeroelastic wing demonstrator
Aeroelastic wing demonstrator equipped with various sensors and actuators in the wind tunnel (left). Distribution of sensors and actuators in the wing planform (right).
Image: 1/2, Credit:
Fig. 5: Impressions of the model instrumentation
Wing shells and closed model
Image: 2/2, Credit:

The wind tunnel test

In addition to the aeroelastic wing demonstrator, additional elements are required for testing novel control and monitoring concepts. A setup was specifically developed in the wind tunnel, here the DNW-NWB low-speed wind tunnel in Braunschweig, which can modify the inflow conditions by generating gusts and/or turbulent flows using a so-called gust generator (see Fig. 6) [3]. The gust generator itself was originally designed and built as part of the DLR oLAF project and further developed within the SAFER² project to now consist of four rotating slotted cylinders. These can be controlled independently of each other using an in-house developed software, which enables generating complex flow conditions. The induced gusts are recorded and measured in real time using an unsteady 5-hole pressure probe mounted above the wing model.

Fig. 6: Experimental setup in the wind tunnel test section
Image: 1/2, Credit:
Layout sketch of the DNW-NWB low-speed wind tunnel in Braunschweig
Image: 2/2, Credit:

The signals from the sensors and actuators, the external measuring systems (i.e., load balance and 5-hole probe), and the gust generator are all observed in an online monitoring setup. This allows colleagues to not sit directly next to or under the wind tunnel, but instead observe the test conditions comfortably from the wind tunnel's control room. From there, the gust generator and all model sensors and actuators can be triggered using remote control. This setup not only allows measurement data as well as the flow and model behavior to be monitored in real time, but also enables safety-relevant parameters, such as aerodynamic loads acting on the model, to be controlled on demand, potentially using load-alleviation controllers. As an example, the online monitoring interface of our unsteady data measurement system is shown in Fig. 8, which depicts model forces and moments, accelerations (i.e., dynamic vibrations/oscillations), and gust characteristics, e.g., gust angle and amplitude.

Fig. 7: Online monitoring interface for unsteady data acquisition in real time

Characterization of innovative sensor model fusion technologies

Such a dedicated experimental setup is needed in order to first test and then evaluate novel, complex control, monitoring, and data reconstruction algorithms, as these methods require a wealth of reliable, real-time processable data signals from sensors to actuate the wing’s control surfaces in a targeted manner. Therefore, the SAFER² test setup allows colleagues from various institutes and departments to experimentally investigate their numerically developed models on a representative aeroelastic wing.

Several core technologies were explored and evaluated within the course of two wind tunnel test campaigns:

  • Reducing gust-induced aerodynamic loads on the wing model using robust [4] and/or reinforcement-learning-based controllers (Institute of Aeroelasticity)
  • Online monitoring of structural modal parameters (e.g., wing damping) of the aeroelastic wing in real time using data fusion [5] (Institute of Aeroelasticity)
  • Destabilizing and re-stabilizing the aeroelastic wing in a safe manner by controlling its damping using a mode-blending control algorithm [6] (Institute of Aeroelasticity)
  • Reconstructing the surface pressure distribution on the aeroelastic wing based on discrete measurement points using machine-learning and data-fusion-based mapping algorithms [7] (Institute of Aerodynamics and Flow Technology)
  • Testing of a real-time monitoring of wing strains and deformations using optical fibers embedded in the wing model (Institute of Lightweight Structures)

The way forward

As part of the impulse project SAFER², a number of innovative technologies were developed, tested, and evaluated, delivering promising results for their potential application in future aircraft systems. These core technologies are being pursued further in national and international projects, including the following ongoing projects:

  • EU project UP Wing: Active control for gust load alleviation under transonic flow conditions
  • Project ACTIVATE: Online monitoring of modal parameters in real time and (semi-)active flutter control
  • Project LevelUP: Real-time reconstruction of wing pressure distribution in flight testing

Referenzen

  1. Schmidt, T. G. et al. Assessing Sensor-Model-Fusion Technologies on a Flexible Aeroelastic Wing Demonstrator through Wind Tunnel Testing in the DLR Project SAFER2. in Deutscher Luft- und Raumfahrtkongress 2025, Augsburg, Germany, 2025.
  2. Schmidt, T. G., Dillinger, J., Ritter, M., Altkuckatz, A. & Braune, M. Design and Experimental Characterization of a Gust-Generator Concept with Rotating-Slotted Cylinders in the Low-Speed Wind Tunnel DNW-NWB. in International Forum on Aeroelasticity and Structural Dynamics 2024, Den Haag, Netherlands, 2024.
  3. Dillinger, J., Meddaikar, Y. M., Lepage, A. & Fabbiane, N. Structural optimization of an aeroelastic wind tunnel model for unsteady transonic testing. CEAS Aeronautical Journal, Vol. 13, 2022.
  4. Stalla, F., Looye, G., Pusch, M. & Theodoulis, S. Integrating Aeroelastic and Primary Flight Control: Robust Design and Wind Tunnel Demonstration. in AIAA SciTech Forum 2026, Orlando, FL, USA, 2026.
  5. Volkmar, R. Intelligent Modal Analysis System: Autonomous Identification and Tracking of Aircraft Modal Parameters. PhD Dissertation. TU Braunschweig, Braunschweig, Germany, 2025.
  6. Micheli, B., Volkmar, R., Soal, K., Tang, M. & Boswald, M. Design and Experimental Validation of a Damping Augmentation Controller via a Saturated Destabilizing/Stabilizing Approach. in 28th AIDAA International Congress, Florence, Italy, 2025. (https://elib.dlr.de/220677/).
  7. Barklage, A., Yilmaz, E. & Bekemeyer, P. Online monitoring of data fusion results in wind tunnel applications. in AIAA SciTech Forum 2026, Orlando, FL, USA, 2026.

Autor:

Dr. Thomas G. Schmidt, Abteilung Aeroelastische Experimente, DLR-Institut für Aeroelastik

Abteilung Aeroelastische Experimente

Die Abteilung Aeroelastische Experimente widmet sich der Planung, Durchführung und Analyse von Windkanal-Experimenten.

Kontakt

Dr. rer. nat. Marc Braune

Leitung Aeroelastische Experimente
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Institut für Aeroelastik
Bunsenstraße 10, 37073 Göttingen

Dr.-Ing. Virginie Chenaux

Leitung Aeroelastische Experimente
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Institut für Aeroelastik
Bunsenstraße 10, 37073 Göttingen