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A framework for enhanced decision-making in aircraft conceptual design optimisation under uncertainty

Published online by Cambridge University Press:  21 December 2020

D.H.B. Di Bianchi Open the ORCID record for D.H.B. Di Bianchi [Opens in a new window] ,
N.R. Sêcco  and
F.J. Silvestre
D.H.B. Di Bianchi*
Affiliation:
Instituto Tecnológico de Aeronáutica, Aeronautical Design, Aerospace Systems and Structures, São José dos Campos, Brazil
N.R. Sêcco
Affiliation:
Instituto Tecnológico de Aeronáutica, Aeronautical Design, Aerospace Systems and Structures, São José dos Campos, Brazil
F.J. Silvestre
Affiliation:
Technische Universität Berlin, Flight Mechanics, Flight Control and Aeroelasticity, Berlin, Germany
*
[email protected]
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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.

Keywords

Optimisation under uncertaintyAircraft conceptual designVisual analyticsDecision-making
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1287 , May 2021 , pp. 777 - 806
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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