Multidisciplinary Integrated Framework for the Optimal Design of a Jet Aircraft Wing

The preliminary design of a jet aircraft wing, through the use of an integrated multidisciplinary design environment, is presented in this paper. A framework for parametric studies of wing structures has been developed on the basis of a multilevel distributed analysis architecture with a “hybrid strategy” process that is able to perform deterministic optimizations and tradeoff studies simultaneously. The particular feature of the proposed multilevel optimization architecture is that it can use different set of variables, defined expressly for each level, in a multi-level scheme using “low fidelity” and “high fidelity” models, as well as surrogate models. The prototype of the design environment has been developed using both commercial codes and in-house tools and it can be implemented in a geographically distributed and heterogeneous IT context. 1. Introduction Aerospace engineering is characterized by great complexity of the systems to be designed and managed. This complexity is due basically to the fact that large-scale systems are considered as well as design problems characterized by strong interactions among the subsystems and the disciplinary analyses involved. Moreover some design requirements are particularly demanding, especially for aircraft industries: in particular, the need to maintain competitiveness, fundamentally in terms of design quality and of reduction of the time to market, is a critical issue. A good response to these requirements can be found through the use of the tools and methodologies gathered under the name of Concurrent Engineering (CE). These strategies focus on the integration of the design and development phases, being the crucial point that has to be solved in order to accomplish competitiveness requirements. The translation of this strategy into an integrated design language is known as Multidisciplinary Analysis and Optimization (MAO) methodologies. These techniques have been developed to achieve a global optimum for multidisciplinary systems [1–3]. Designers and researchers have noted that the so-called “all-at-once” technique suffers from practical limitations and is confined to only small, simplified problems, at a conceptual level. Designers have investigated optimization methods in several engineering fields over the last few years. These methods include decomposition strategies, evolutionary and mimetic algorithms, approximation techniques, response surfaces methodology, robust analysis, and reliability-based optimization. Good surveys about these topics can be found in [4–8], while an example of the use of