Application of MDO to Large Subsonic Transport Aircraft

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(c)2000 American Institute of Aeronautics & Astronautics or published with permission of author(s) and/or author(s)’ sponsoring organization. Alexander Van der Velden Roland Kelm David Kokan Josef Mertens AMA-2000-0844 Application of MD0 to Large Subsonic Transport Aircraft Synaps, Inc., velden@synaps-inc.com president- Daimler-Chrysler Aerospace Airbus, Hamburg, Germany head aeroelastic structures Synaps, Inc. senior consultant Daimler-Chrysler Aerospace Airbus, Bremen, Germany RAWID project manager aerophysics division Daimler-Chrysler Aerospace Airbus and Synaps developed a preliminary design tool to quickly trade-off wing planform, thickness and lift distribution on a large commercial passenger transport wing. Goal of this study was to minimize the weight of the wing structure + fuel at a constant maximum takeoff weight. The design study was performed with the PointerTM MD0 framework software. Pointerm couples the required analysis codes and searched the design space for the best solution. The section drag was determined using a large database of swept airfoils that were shape optimized for specific design conditions. The wing loads and induced drag were determined with a vortex-lattice code. The weight of the wing was estimated using the Airbus FAME software which sizes the wing primary and secondary structure based on the wing loads with static aero-elastic effects. The present method used led to significant performance improvements (which were confirmed by later detailed studies) and a better understanding of what drives the design of very large transports. As compared to conventional design iterations performed by teams of experts, the present method requires only half the time per design cycle and a fraction of the number of design cycles to converge the design. Introduction The design of a new very large transport [Fig. 1, Ref. 131 poses several challenges to the conventional method. In the conventional approach, the preliminary design department proposes an initial description of the design to the aerodynamic design team. This team finishes a full aerodynamic design optimization for this configuration and then passes this detailed geometry to the structural design group who compute the required weights. However, in the case of very large aircraft, many detailed design cycle iterations (>lO) were required to converge to an optimal design. One of the reasons for that is because the initial preliminary design does not include the right level of physical modeling to trade-off design decisions for a very large transonic aircraft. 1 American Institute for Aeronautics and Astronautics Fig 1: Proposal for a new large aircraft (photo Airbus industrie) This problem is the most pressing for wing design. Therefore, in the past 6 years significant effort has been invested to create preliminary design tools that accurately predict the aerodynamic and structural potential of well designed transonic aircraft wings. With these
(c)2000 American Institute of Aeronautics & Astronautics or published with permission of author(s) and/or author(s)’ sponsoring organization. tools it is possible to make accurate tradeoff studies using multi-disciplinary optimization technology in the initial stage of aircraft definition. This tool is similar to the MIDAS tool [5] developed in the early ‘90s to design the Present Method In order to increase the accuracy of the initial preliminary design trade-off, we developed a new wing preliminary design tool. This is achieved by increasing the physical fidelity of the models and by using multi-disciplinary design optimization. This tool was comprised out of three parts: a) Pointer MD0 framework. The purpose of this tool is to execute computer simulations more efficiently, and to search the design space created with the outputs of these simulations for the best solution. b) FAME-W weight and loads model developed by DASA-Airbus Hamburg. It determines the potentially best structural weight for a well designed aero-elastically deforming wing using only a preliminary description of the structure and ,aircraft mission. c) Globair aerodynamics model developed by Synaps and DASA-Airbus Bremen. It determines the potentially lowest transonic drag for a well designed wing for a given mission point using only a preliminary description of the wing surface. Pointer as a tool to perform design studies. Pointer [6,9] is an MD0 framework tool. MD0 framework tools are explained in detail by Salas and Townsend in ref. [7]. The development of Pointer was driven by the following consideration: Pointer should give the expert user the ability to find a better answer to a wide class of complex design problems in less time than it would take him to solve the problem in a conventional way. 2 American Institute for Aeronautics and Astronautics DASA & European supersonic transports. However because subsonic transports are much more mature, a much higher accuracy in the physical modeling is required to achieve (typically smaller) performance gains. Therefore, the setup of the design problem should be as user-friendly as possible. In terms of the software architecture Pointer achieves this by using intuitive GUI’s [Fig 21, object oriented principles and the use of software standards. Pointer automatically parses the parameters defining the input to legacy and proprietary codes. Pointer also controls the running of these codes both in sequence and parallel. After all the I/O is defined, the design problem is stated in the form of an objective, design variables and constraints. It is here that most users have problems and we consider this the major hurdle in the development of MD0 technology. In his paper “On making things best” [lo] Holt Ashley said it all, when he stated the omission of the optimization algorithm description in a design study a “charming sign of maturity”. In Pointer a hybrid combination of the best current optimization algorithms [Linear Programming, Sequential Quadratic Programming, Gradient, Downhill-simplex and Genetic] are used to solve the problem. The program does not require any intervention from the user or specialist know- ledge about optimization. In our experience, Pointer requires T hours to solve the problem: T = Z * #variables *Time-per-design- simulation Z, the relative complexity of the problem, is typically 100, the first time a strongly non-linear engineering problem is solved. We see the initial investment of so many function calls as essential to understanding the problem posed by the topography of the objective function. In practice if the computer simulation takes more than I hour, parallel processing of the design simulation is required to avoid excessive run-times. In subsequent uses, Pointer learns from its experience and typically reduces Z to a number below 10. This saves a lot of time when optimizations are repeated, for instance when an optimal design database is created.
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Application of MDO to Large Subsonic Transport Aircraft

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