Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Calculating operational availability (Ao) for a system of systems (SoS) presents unique challenges to reliability, availability, and maintainability (RAM) assessment, modeling, and analysis. System interdependencies and complex interrelated sustainment operations that exist in a SoS present complexities that must be accounted for in calculating or estimating Ao for the SoS. These system interdependencies affect the operating, operable, and down times of the individual systems. Both system-level and SoS-level Ao performance must be assessed within the SoS context for logistics and planning purposes. However, metrics calculated for the individual systems as part of the SoS may not be appropriate for assessing the individual system performance against their individual system requirements. In most cases, simulation modeling is required to capture the complex operating, operable, and down time hours of a SoS and the systems in the SoS, and to accurately aggregate the individual system availabilities to higher SoS levels. This paper explores some of the complexities involved in SoS Ao modeling and presents So S simulation results from a modeled SoS application.

Abstract not provided.

Abstract not provided.

This report documents the results of an LDRD program entitled ''Network and Adaptive System of Systems Modeling and Analysis'' that was conducted during FY 2005 and FY 2006. The purpose of this study was to determine and implement ways to incorporate network communications modeling into existing System of Systems (SoS) modeling capabilities. Current SoS modeling, particularly for the Future Combat Systems (FCS) program, is conducted under the assumption that communication between the various systems is always possible and occurs instantaneously. A more realistic representation of these communications allows for better, more accurate simulation results. The current approach to meeting this objective has been to use existing capabilities to model network hardware reliability and adding capabilities to use that information to model the impact on the sustainment supply chain and operational availability.

Analyzing the performance of a complex System of Systems (SoS) requires a systems engineering approach. Many such SoS exist in the Military domain. Examples include the Army's next generation Future Combat Systems 'Unit of Action' or the Navy's Aircraft Carrier Battle Group. In the case of a Unit of Action, a system of combat vehicles, support vehicles and equipment are organized in an efficient configuration that minimizes logistics footprint while still maintaining the required performance characteristics (e.g., operational availability). In this context, systems engineering means developing a global model of the entire SoS and all component systems and interrelationships. This global model supports analyses that result in an understanding of the interdependencies and emergent behaviors of the SoS. Sandia National Laboratories will present a robust toolset that includes methodologies for developing a SoS model, defining state models and simulating a system of state models over time. This toolset is currently used to perform logistics supportability and performance assessments of the set of Future Combat Systems (FCS) for the U.S. Army's Program Manager Unit of Action.

This report documents the results of an LDRD program entitled 'System of Systems Modeling and Analysis' that was conducted during FY 2003 and FY 2004. Systems that themselves consist of multiple systems (referred to here as System of Systems or SoS) introduce a level of complexity to systems performance analysis and optimization that is not readily addressable by existing capabilities. The objective of the 'System of Systems Modeling and Analysis' project was to develop an integrated modeling and simulation environment that addresses the complex SoS modeling and analysis needs. The approach to meeting this objective involved two key efforts. First, a static analysis approach, called state modeling, has been developed that is useful for analyzing the average performance of systems over defined use conditions. The state modeling capability supports analysis and optimization of multiple systems and multiple performance measures or measures of effectiveness. The second effort involves time simulation which represents every system in the simulation using an encapsulated state model (State Model Object or SMO). The time simulation can analyze any number of systems including cross-platform dependencies and a detailed treatment of the logistics required to support the systems in a defined mission.

Abstract not provided.