A flexible, open-structured computer based approach for aircraft conceptual design optimisation

2002 ◽  
Author(s):  
F. Schieck ◽  
N. Deligiannidis ◽  
T. Gottmann
10.14311/272 ◽  
2001 ◽  
Vol 41 (4-5) ◽  
Author(s):  
F. Schieck ◽  
D. Schmitt

In the early design stages of a new aircraft, there is a strong need to broaden the knowledge base of the evolving aircraft project, allowing a profound analysis of the solution concepts and of the design driving requirements. The methodology presented in this paper provides a tool for increasing and improving in an exemplary manner the necessary information on cargo aircraft. By exchanging or adapting a few particular modules of the entire program system, the tool is applicable to a range scale of different aircraft types. In an extended requirement model, performance requirements are represented along with other operational requirements. An aircraft model is introduced in sufficient detail for conceptual design considerations. The computer-aided scaling methodology is explained, which, controlled by an optimisation module, automatically resizes the aircraft model until it optimally satisfies the requirements in terms of a selectable figure of merit. Typical results obtained at the end of the scaling are discussed together with knowledge gained during the process, and an example is given.


2020 ◽  
pp. 1-30
Author(s):  
D.H.B. Di Bianchi ◽  
N.R. Sêcco ◽  
F.J. Silvestre

Abstract This paper presents a framework to support decision-making in aircraft conceptual design optimisation under uncertainty. Emphasis is given to graphical visualisation methods capable of providing holistic yet intuitive relationships between design, objectives, feasibility and uncertainty spaces. Two concepts are introduced to allow interactive exploration of the effects of (1) target probability of constraint satisfaction (price of feasibility robustness) and (2) uncertainty reduction through increased state-of-knowledge (cost of uncertainty) on design and objective spaces. These processes are tailored to handle multi-objective optimisation problems and leverage visualisation techniques for dynamic inter-space mapping. An information reuse strategy is presented to enable obtaining multiple robust Pareto sets at an affordable computational cost. A case study demonstrates how the presented framework addresses some of the challenges and opportunities regarding the adoption of Uncertainty-based Multidisciplinary Design Optimisation (UMDO) in the aerospace industry, such as design margins policy, systematic and conscious definition of target robustness and uncertainty reduction experiments selection and prioritisation.


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