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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