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Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Park, Hyeong-Uk;Chung, Joon;Lee, Jae-Woo

  • Received : 2015.09.14
  • Accepted : 2016.06.20
  • Published : 2016.06.30

Abstract

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

Keywords

Reliability Based Design Optimization;Possibility Based Design Optimization;Aircraft Conceptual Design;Derivative Design

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Acknowledgement

Supported by : NSERC (National Sciences and Engineering Research Council of Canada), National Research Foundation of Korea