3577. Method For The Reduction Of Analytical Weight Estimation Models For The Use In Early Project Phases


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Daniel Lindner: 3577. Method For The Reduction Of Analytical Weight Estimation Models For The Use In Early Project Phases. 2012.



Weight estimation is a very important topic during all phases of aircraft development. It is indispensible for the estimation of loads and aircraft inertias to have a funded estimation of the weight of the aircraft’s components and the position of their center of gravity. A realistic estimation can be quite complicated especially during very early stages of aircraft development. Additionally, due to the increasing level of available information on the new design, it is necessary to change the method and tool for the estimation during the development process. However, this change of methodology cannot inflict major changes on the estimated weight for a specific component. Neither can it change the sensitivities of the component’s weight to the main sizing parameters. It is important to guarantee the continuity of the estimation and the sensitivities over the whole tool chain.
This paper shows a top down approach to the development of weight estimation methods for very early project phases. In order to being able to guarantee the continuity of the estimation, the model for early phases is developed as a reduced model of an existing high level analytical estimation tool. Through statistical analysis the main driving parameters of the top level method are determined. A reduced model is being developed based on those parameters and the sensitivities of the top model with respect to those parameters. This way both, the continuity of the estimated weight and the continuity of the major sensitivities between the reduced and the top level model, can be guaranteed. The methodology is described theoretically and applied to the estimation of the pylon primary structure weight. This component is very important for trade off studies in early phases with respect to the engine position. Therefore it is necessary to have good sensitivities for this estimation method. For the model reduction process itself a mathematical and a physical approach have been performed and the results analyzed and compared. Finally, the paper gives methods for checking the correct modeling of the major sensitivities compared to the top level tool.


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