Abstract
One approach used by NASA when evaluating a science mission architecture is to develop a Science Traceability Matrix (STM), which presents a flow-down of mission objectives into science objectives, measurement objectives, and measurement and instrument requirements. Recently, the science traceability and alignment framework (STAF) was proposed as an improvement over the STM. One limitation of both approaches used for traceability purposes is that they must be manually completed by experts and thus cannot be used to compare many alternatives as done in trade space exploration. This can be addressed to some extent using formal modeling languages such as SysML and model-based systems engineering (MBSE) tools, which can automatically construct tables showing traceability between requirements. However, despite the presence of parametric diagrams, it is still challenging to quantify how changes in the system architecture affect requirement satisfaction. Here, the authors present a knowledge-based tool that can address both needs: given a system architecture, it can generate traceability hierarchies similar to the ones in STM and STAF while also providing a quantitative assessment of requirement satisfaction. This executable STM is implemented as a rule-based system that uses a database of measurement requirements for a given mission. Upon execution, the tool provides not only a score but also a rationale for full or degraded satisfaction of the mission requirements. As an example, the three Mars mission architectures proposed in the Decadal Survey were evaluated. Traceability to the objectives established by the Decadal Survey along with the science value achieved by each concept and the rationale for why the missions cannot satisfy certain measurements were generated.
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