ALBERT

Description: As the durations and distances involved in human exploration missions increase, the logistics associated with the repair and maintenance becomes more challenging. Whereas the operation of the International Space Station (ISS) depends upon regular resupply from the Earth, this paradigm may not be feasible for future missions. Longer mission durations result in higher probabilities of component failures as well as higher uncertainty regarding which components may fail, and longer distances from Earth increase the cost of resupply as well as the speed at which the crew can abort to Earth in the event of an emergency. As such, mission development efforts must take into account the logistics requirements associated with maintenance and spares. Accurate prediction of the spare parts demand for a given mission plan and how that demand changes as a result of changes to the system architecture enables full consideration of the lifecycle cost associated with different options. In this paper, we utilize a range of analysis techniques - Monte Carlo, semi-Markov, binomial, and heuristic - to examine the relationship between the mass of spares and probability of loss of function related to the Carbon Dioxide Removal System (CRS) for a notional, simplified mission profile. The Exploration Maintainability Analysis Tool (EMAT), developed at NASA Langley Research Center, is utilized for the Monte Carlo analysis. We discuss the implications of these results and the features and drawbacks of each method. In particular, we identify the limitations of heuristic methods for logistics analysis, and the additional insights provided by more in-depth techniques. We discuss the potential impact of system complexity on each technique, as well as their respective abilities to examine dynamic events. This work is the first step in an effort that will quantitatively examine how well these techniques handle increasingly more complex systems by gradually expanding the system boundary.

Description: Under a changing technological and economic environment, there is growing interest in implementing future NASA Earth Science missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: Which type of constellations should be chosen? How many spacecraft should be included in the constellation? Which design has the best costrisk value? This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI and formulating Trade-space Search Requests for the Trade-space Search Iterator, which in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules generates multiple potential architectures and their associated characteristics. UI will include Graphical, Command Line and Application Programmer Interfaces to respond to the demands of various levels of users expertise. Science inputs are grouped into various mission concepts, satellite specifications, and payload specifications, while science outputs are grouped into several types of metrics - spatial, temporal, angular and radiometric. Orbit Coverage leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data that are passed to Reduction Metrics. Then, for each architecture design, Cost Risk will provide estimates of the cost and life cycle cost as well as technical and cost risk of the proposed mission. Additionally, the Knowledge Base module is a centralized store of structured data readable by humans and machines. It will support both TAT-C analysis when composing new mission concepts from existing model inputs, and TAT-C exploration when discovering new mission concepts by querying previous results.

Description: Traditionally, space missions have relied on relatively large and monolithic satellites, but in the past few years, under a changing technological and economic environment, including instrument and spacecraft miniaturization, scalable launchers, secondary launches as well as hosted payloads, there is growing interest in implementing future NASA missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: How many spacecraft should be included in the constellation? Which design has the best costrisk value? The main goals of TAT-C are to: Handle multiple spacecraft sharing a mission objective, from SmallSats up through flagships, Explore the variables trade space for pre-defined science, cost and risk goals, and pre-defined metrics Optimize cost and performance across multiple instruments and platforms vs. one at a time.This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI, then formulating Trade-space Search Requests for the Trade-space Search Iterator first with inputs from the Knowledge Base, then, in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules, generating multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, streamlining the computations by modeling orbits in a way that balances accuracy and performance.TAT-C current version includes uniform Walker constellations as well as Ad-Hoc constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The Knowledge Base supports both analysis and exploration, and the current GUI prototype automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost.