Ground and Range Operations for a Heavy-Lift Vehicle: Preliminary Thoughts

This paper discusses the ground and range operations for a Shuttle derived Heavy-Lift Vehicle being launched from the Kennedy Space Center on the Eastern range. Comparisons will be made between the Shuttle and a heavy lift configuration (SLS-ETF MPCV – April 2011) by contrasting their subsystems. The analysis will also describe a simulation configuration with the potential to be utilized for heavy lift vehicle processing/range simulation modeling and the development of decision-making systems utilized by the range. In addition, a simple simulation model is used to provide the required critical thinking foundations for this preliminary analysis. INTRODUCTION Simulation modeling is one of the most important areas for exploration. The NASA Office of Chief Technologist (OCT) [6] has stated that “Simulation focuses on the design, planning, and operational challenges of NASA’s distributed, long-lived mission systems.” We agree that a model represents the features of a system from a dimensional or multidimensional viewpoint. On the other hand, simulation is the execution of a model which has the possibility (if the model is able to capture appropriately the features up to certain level of fidelity) to represent its behavior. In addition, OCT states [6] that “Through the combination of the two, we can make better decisions and communicate those decisions early enough in the design and development process that changes are easy and quick, as opposed to during production when they are extremely costly and practically impossible.” There are several principles with complex systems such as “emergent behavior” which can be discovered with simulation. Simulation modeling has some interesting benefits and features: 1. It helps to understand complex problems from different viewpoints: We have to understand the system and its structure, goals and objectives. We have to view complex problems from different dimensions. It is usual a multi-disciplinary effort. 2. Basic theory can be combined with experiments and expert opinions: A simulation model can fused data and information from first-principle models, empirical, and expert opinions. 3. Ontologies will be very important to increase agility and interoperability: This will support the development of knowledge discovery mechanisms and the potential automation of generating simulation models. 4. A map of models is important: A map of “models” (analytical and empirical) from the different points in the life-cycle of a system is an important endeavor in order to determine gaps.