The introduction of concurrent design practices to the aerospace industry has greatly increased the efficiency and productivity of engineers during design sessions. Teams that are well-versed in such practices such as JPL's Team X are able to thoroughly examine a trade space and develop a family of reliable point designs for a given mission in a matter of weeks compared to the months or years sometimes needed for traditional design. Simultaneously, advances in computing power have given rise to a host of potent numerical optimization methods capable of solving complex multidisciplinary optimization problems containing hundreds of variables, constraints, and governing equations. Unfortunately, such methods are tedious to set up and require significant amounts of time and processor power to execute, thus making them unsuitable for rapid concurrent engineering use. In some ways concurrent engineering and automated system-level optimization are often viewed as being mutually incompatible. It is therefore desirable to devise a system to allow concurrent engineering teams to take advantage of these powerful techniques without hindering the teams' performance. This paper proposes such an integration by using parametric approximations of the subsystem models. These approximations are then linked to a system-level optimizer that is capable of reaching a solution more quickly than normally possible due to the reduced complexity of the approximations. The integration structure is described in detail and applied to a standard problem in aerospace engineering. Further, a comparison is made between this application and traditional concurrent engineering through an experimental trial with two groups each using a different method to
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