Numerical simulation is already an important cornerstone for aircraft design, although the application of highly accurate methods is mainly limited to the design point. To meet future technical, economic and social challenges in aviation, it is essential to simulate a real aircraft at an early stage, including all multidisciplinary interactions covering the entire flight envelope, and to have the ability to provide data with guaranteed accuracy required for development and certification. However, despite the considerable progress made there are still significant obstacles to be overcome in the development of numerical methods, physical modeling, and the integration of different aircraft disciplines for multidisciplinary analysis and optimization of realistic aircraft configurations. At DLR, these challenges are being addressed in the framework of the multidisciplinary project Digital-X (4/ 2012-12/2015). This paper provides an overview of the project objectives and presents first results on enhanced disciplinary methods in aerodynamics and structural analysis, the development of efficient reduced order methods for load analysis, the development of a multidisciplinary optimization process based on a multi-level/variable-fidelity approach, as well as the development and application of multidisciplinary methods for the analysis of maneuver loads.
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
The expected computational power that will become available in the next years and decades will allow the introduction of more accurate simulations at earlier aircraft design stages. It is thus mandatory to identify and consequently develop multi-disciplinary optimization capabilities based on high-fidelity methods enabling the design of the future aircraft. The paper will give an overview of the latest development conducted at DLR in this field. Three representative applications will demonstrate benefits and limitations of the capabilities developed. Nomenclature FFD= Free-Form Deformation CD = Drag Coefficient CFD = Computational Fluid dynamics CL = Lift coefficient C SFC = Thrust Specific Fuel Consumption DC = Drag Counts (1DC=0.0001) HTP = Horizontal Tail Plane M = Cruise Mach Number MTOW = Maximum Take-Off Weight RANS = Reynolds Averaged Navier-Stokes Equations Re = Reynolds Number VTP = Vertical Tail Plane W = Weight
The aerodynamic inverse design approach consists of finding the geometry that produces a desired (target) pressure distribution. Such design problem is here solved using optimization strategies based on CFD solver to minimize the pressure residual. It is observed that the key ingredients for solving this problem are the mesh point parametrization combined with the adjoint approach for efficient and reliable design. The resulting optimization framework is finally successfully assessed on representative 2D design problems ranking from viscous subsonic to inviscid supersonic flows.
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