In this paper, the aeroservoelastic modeling of a highly flexible flutter demonstrator is presented. A finite element model of the demonstrator is generated and condensed to a reduced number of degrees of freedom to represent the structural dynamics. The unsteady aerodynamics are captured by the doublet lattice method based on potential theory. By interconnection of structural dynamics and unsteady aerodynamics an aeroelastic model is derived, which provides a basis for the design of a flutter suppression controller. In order to enable an efficient flutter suppression a clear separation of the occurring flutter mechanisms in speed and frequency is desired. To achieve this, the positions of the actuators controlling the flaps are varied within the scope of the aircraft design process. Due to their large mass contribution, the placement of the actuators has a crucial impact on the overall flutter characteristics and optimal actuator positions are determined by means of a mass sensitivity study.

This paper addresses the development of aircraft models for flight loads analysis in the pre-design stage. The underlying model structure consists of the nonlinear equations of motion of a free flying, flexible aircraft, as well as a model, which calculates the distributed aerodynamics over the entire airframe.Different possibilities in modelling the unsteady aerodynamic interactions for pre-design purposes are explored and the effects on the loads are compared in order to assess the tradeoffs between accuracy and speed. The following methods are modelled and compared:• a quasi-steady Vortex Lattice Method (VLM) without any further unsteady improvements,• an extended strip theory, where unsteady effects are modelled by indicial functions (IFM) such as Wagner's and Küssner's function,• and a Rational Function Approximation according to Roger's Method of the unsteady Doublet Lattice Method (DLM).

This article gives an overview of reduced order modeling work performed in the DLR project Digital-X. Parametric aerodynamic reduced order models (ROMs) are used to predict surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady aerodynamic applications are built using proper orthogonal decomposition (POD) and Isomap, a manifold learning method. Approximate solutions in the so obtained low-dimensional representations of the data are found with interpolation techniques, or by minimizing the corresponding steady flow-solver residual. The latter approach produces physics-based ROMs driven by the governing equations. The steady ROMs are used to predict the static aeroelastic loads in a multidisciplinary design and optimization (MDO) context, where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. An approach to correct a linear loads analysis model using steady CFD solutions at various Mach numbers and angles of attack and a ROM of the corrected Aerodynamic Influence Coefficients (AICs) is also shown. This results in a complete loads analysis model preserving aerodynamic nonlinearities while allowing fast evaluation across all model parameters. The different ROM methods are applied to a 3D test case of a transonic wing-body transport aircraft configuration. Keywords reduced order model • proper orthogonal decomposition • isomap • manifold learning • multidisciplinary design and optimization • aerodynamic influence coefficients • loads analysis • CFD

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Structural weight reduction and high aspect ratio wings play a key role in improving the performance of modern transport aircraft. This leads to a highly flexible aircraft structure which is sensitive to external disturbances like gusts. To counteract this undesired effect, active control is a promising technology. In this paper, a gust load alleviation controller is designed for a wind tunnel model of a flexible wing with various trailing edge flaps and acceleration sensors. For a sophisticated model-based controller design, a detailed aeroelastic model is derived describing the coupling of structural dynamics and aerodynamics. Additionally, actuator dynamics and structural modes are identified and used to improve model accuracy. Subsequently, the weakly damped first wing bending mode, which causes high structural loads, is isolated via H 2-optimal blending of control inputs and measurement outputs. In this way, a gain-scheduled single-input single-output controller can be designed to control the desired aeroelastic mode. Eventually, the great potential of the proposed control approach is verified by a wind tunnel test including different gust excitations and varying airspeeds.

The paper presents the control design approaches for the European research project FLEXOP. The ultimate goal is to develop and apply active flutter suppression and load alleviation techniques on an unmanned flying aircraft demonstrator. Due to the flexible wing of the aircraft new challenges rise for the control design: the traditional rigid body (baseline) control loops have to be augmented with flutter control laws. In our approach, the controllers are designed based on a dynamical model, which is briefly discussed first. Details of the baseline control design, as well as the two different flutter suppression algorithms are discussed in the paper. Hardware-in-the-Loop testing of the controllers are reported before the first test flights of the aircraft.

The paper details the research and corresponding implementation and testing steps of the FLEXOP demonstrator aircraft. Within the EU funded project an unmanned demonstrator aircraft is built to validate the mathematical modelling, flight control design and implementation side of active flutter mitigation. In order to validate the different methods and tools developed in this project, a flight test campaign is planned, in which the design and manufacturing of stiff wings (-0), are compared with very flexible wings (-1) with active flutter control, to see the overall benefit vs. risk of such technology. The mathematical models of the aircraft are first developed using FEM and CFD tools, what are later reduced by model order reduction techniques. The high-fidelity models are updated using Ground Vibration Test results. Manufacturing tolerances and variations in aircraft parameters are captured by systematic modelling of parametric and dynamic uncertainties. Both the simulation environment and the control design framework use different modelling fidelity, what are described within the paper. Reduced models are developed using two distinctive methods, respecting the control design needs: top-down balanced LPV reduction and bottom-up structure preserving methods. Based on the reduced order models various control design techniques have been elaborated by the consortium partners. In particular DLR developed and implemented a modal control method using H2 optimal blends for inputs and outputs. University of Bristol developed structured H-infinity optimal control methods, while SZTAKI proposed a worst-case gain optimal method structured controller synthesis method handling parametric and complex uncertainties. After the brief introduction of hardware-in-the-loop test setup and the description of mission scenarios the implementation issues of the baseline and flutter controllers are discussed. DLR and SZTAKI flutter controllers are evaluated in a hybrid software-/ hardware-in-the-loop test setup as at this stage of development the latter can not tolerate the estimated delay of the hardware system but their comparison is advantageous before future developments. Recommendations on active flutter mitigation methods are given based on the experience of synthesis and implementation of these controllers. Flight test results will follow these experiments, once the flight testing of the flutter wing commences.