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NASA's Advanced Engineering Environment (AEE) is a research and development program that will improve collaboration among design engineers for launch vehicle conceptual design and provide the infrastructure (methods and framework) necessary to enable that environment. In this paper, three major technical challenges facing the AEE program are identified, and three specific design problems are selected to demonstrate how advanced methods can improve current design activities. References are made to studies that demonstrate these design problems and methods, and these studies will provide the detailed information and check cases to support incorporation of these methods into the AEE. This paper provides background and terminology for discussing the launch vehicle conceptual design problem so that the diverse AEE user community can participate in prioritizing the AEE development effort.

NASA's Advanced Engineering Environment (AEE) is a research and development program that will improve collaboration among design engineers for launch vehicle conceptual design and provide the infrastructure (methods and framework) necessary to enable that environment. In this paper, three major technical challenges facing the AEE program are identified, and three specific design problems are selected to demonstrate how advanced methods can improve current design activities. References are made to studies that demonstrate these design problems and methods, and these studies will provide the detailed information and check cases to support incorporation of these methods into the AEE. This paper provides background and terminology for discussing the launch vehicle conceptual design problem so that the diverse AEE user community can participate in prioritizing the AEE development effort.

The problem is to find a global minimum for the Problem P. Necessary and sufficient conditions are available for local optimality. However, global solution can be assured only under the assumption of convexity of the problem. If the constraint set S is compact and the cost function is continuous on it, existence of a global minimum is guaranteed. However, in view of the fact that no global optimality conditions are available, a global solution can be found only by an exhaustive search to satisfy Inequality. The exhaustive search can be organized in such a way that the entire design space need not be searched for the solution. This way the computational burden is reduced somewhat. It is concluded that zooming algorithm for global optimizations appears to be a good alternative to stochastic methods. More testing is needed; a general, robust, and efficient local minimizer is required. IDESIGN was used in all numerical calculations which is based on a sequential quadratic programming algorithm, and since feasible set keeps on shrinking, a good algorithm to find an initial feasible point is required. Such algorithms need to be developed and evaluated.