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Collaborative optimization is a new design architecture specically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-dened number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specic issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecture to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the inuence which an a priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the dierence between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecture in a multidisciplinary design environment are also discussed.Aerospace Engineer, Senior member AIAA. y Aerospace Engineer, Senior member AIAA. z Associate Professor, Senior member AIAA.

This paper describes a research program aimed at improved largGscaleacmauh 'cal systems. ' h e research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture. that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication require- summarvAlthough ane often thinks of multidisciplinary opimization of an aircraft configmtion as pceeding logically from geometrical description to disciplinary analyses to performance constraint evaluation, the actual structure of analysesin an aircraft conceptualdesign method is much more complex. Figure 1 shows the connections between just some of the subroutines in one such program (Ref. 1).

▪ Abstract  This article describes some of the fundamental ideas underlying methods for induced-drag prediction and reduction. A review of current analysis and design methods, including their development and common approximations, is followed by a survey of several approaches to lift-dependent drag reduction. Recent concepts for wing planform optimization, highly nonplanar surfaces, and various tip devices may lead to incremental but important gains in aircraft performance. Focusing on relatively high-aspect-ratio subsonic wings, the review suggests that opportunities for new concepts remain, but the greatest challenge lies in their integration with other aspects of the system.

The complex-step method for calculating sensitivities and its use in numerical algorithms is presented. A general procedure for the implementation of this method is described in detail and a script is developed that automates its implementation. The numerical examples include the automatic conversion of a structural finite element and a two-dimensional computational fluid dynamics code. In both of these examples, the complex-step method is compared with other existing methods, namely finitedifferencing, automatic differentiation and an analytic method. The complex-step method is shown to have implementation advantages over automatic differentiation and computational advantages over finite-differencing.

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Abstract-Search and exploration using multiple autonomous sensing platforms has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage or exploration problem, in that the target area needs to be continuously searched, minimizing the time between visitations to the same region. This difference does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. In this research we investigate techniques that are scalable, reliable, efficient, and robust to problem dynamics. These are tested in a multiple unmanned air vehicle (UAV) simulation environment, developed for this program.A semi-heuristic control policy for a single UAV is extended to the case of multiple UAVs using two methods. One is an extension of a reactive policy for a single UAV and the other involves allocation of sub-regions to individual UAVs for parallel exploration. An optimal assignment procedure (based on auction algorithms) has also been developed for this purpose. A comparison is made between the two approaches and a simplified optimal result. The reactive policy is found to exhibit an interesting emergent behavior as the number of UAVs becomes large. The control policy derived for a single UAV is modified to account for actual aircraft dynamics (a 3 degree-of-freedom nonlinear dynamics simulation is used for this purpose) and improvements in performance are observed. Finally, we draw conclusions about the utility and efficiency of these techniques.