Design and Application of the Exploration Maintainability Analysis Tool

Conducting human exploration missions beyond Low Earth Orbit (LEO) will present unique challenges in the areas of supportability and maintainability. The durations of proposed missions can be relatively long and re-supply of logistics, including maintenance and repair items, will be limited or non-existent. In addition, mass and volume constraints in the transportation system will limit the total amount of logistics that can be flown along with the crew. These constraints will require that new strategies be developed with regards to how spacecraft systems are designed and maintained. NASA is currently developing Design Reference Missions (DRMs) as an initial step in defining future human missions. These DRMs establish destinations and concepts of operation for future missions, and begin to define technology and capability requirements. Because of the unique supportability challenges, historical supportability data and models are not directly applicable for establishing requirements for beyond LEO missions. However, supportability requirements could have a major impact on the development of the DRMs. The mass, volume, and crew resources required to support the mission could all be first order drivers in the design of missions, elements, and operations. Therefore, there is a need for enhanced analysis capabilities to more accurately establish mass, volume, and time requirements for supporting beyond LEO missions. Additionally, as new technologies and operations are proposed to reduce these requirements, it is necessary to have accurate tools to evaluate the efficacy of those approaches. In order to improve the analysis of supportability requirements for beyond LEO missions, the Space Missions Analysis Branch at the NASA Langley Research Center is developing the Exploration Maintainability Analysis Tool (EMAT). This tool is a probabilistic simulator that evaluates the need for repair and maintenance activities during space missions and the logistics and crew requirements to support those activities. Using a Monte Carlo approach, the tool simulates potential failures in defined systems, based on established component reliabilities, and then evaluates the capability of the crew to repair those failures given a defined store of spares and maintenance items. Statistical analysis of Monte Carlo runs provides probabilistic estimates of overall mission safety and reliability. This paper will describe the operation of the EMAT, including historical data sources used