3702. Uncertainty Modelling in a Wing Weight Convergence Simulation Framework
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Paper
Abstract
Weight is a key element in aircraft design, having a major influence on its performance and being a common factor to all disciplines involved in the decision making process, i.e. aero- dynamics, structural sizing, materials, loads, geometry, cost, manufacturing, etc. To ensure an optimal trade-off is achieved, alongside a smooth convergence to the desired final aircraft weight, it is essential to be able to model the aircraft weight estimation process throughout the design, including assessment of uncertainty and risk. Weight estimation processes and uncer- tainty analysis are well established bodies of literature. Yet, its unification into a framework that can deliver meaningful managerial information is a new research branch.This paper presents a new methodology for quantifying uncertainty and performing sensi- tivity studies on aircraft weight estimation. A framework has been developed that emulates the weight convergence corridor for an aircraft wing. It combines a traditional wing-box sizing method for primary weight with alternative methods for secondary weight. The alternative methods mimic the different phases of design in the aircraft development cycle. Maturity of design translates to the status of the information available, which translates to accuracy in the weight estimation method in use.This process incorporates uncertainty in the form of modelling the desired input parame- ters as probability density functions (PDFs). The uncertain input space may include wing and engine planform geometry, wing-box material properties, load cases, general aircraft weights and fuselage dimensions. Design features and aircraft components are correlated and there- fore an underlying dependency grid prevails. Combining the PDFs on the grid propagates the uncertainty towards an ultimate distribution of the total wing weight.This paper investigates the use of the framework developed for wing weight estimation by quantifying design sensitivities impact on wing weight. The methodology is demonstrated on a representative commercial jet airliner wing.
