The value proposition of distributed satellite systems for space science missions

Abstract

The resources available for planetary science missions are finite and subject to some uncertainty. Despite decreasing costs of spacecraft components and launch services, the cost of space science missions is increasing, causing some missions to be canceled or delayed, and fewer science groups have the opportunity to achieve their goals due to budget limits. New methods in systems engineering have been developed to evaluate flexible systems and their sustained lifecycle value, but these methods are not yet employed by space agencies in the early stages of a mission's design. Previous studies of distributed satellite systems (DSS) showed that they are rarely competitive with monolithic systems; however, comparatively little research has focused on how DSS can be used to achieve new, fundamental space science goals that simply cannot be achieved with monolithic systems. The Responsive Systems Comparison (RSC) method combines Multi-Attribute Tradespace Exploration with Epoch-Era Analysis to examine benefits, costs, and flexible options in complex systems over the mission lifecycle. Modifications to the RSC method as it exists in previously published literature were made in order to more accurately characterize how value is derived from space science missions. A tiered structure in multi-attribute utility theory allows attributes of complex systems to be mentally compartmentalized by stakeholders and more explicitly shows synergy between complementary science goals. New metrics help rank designs by the value derived over their entire mission lifecycle and show more accurate cumulative value distributions. A complete list of the emergent capabilities of DSS was defined through the examination of the potential benefits of DSS as well as other science campaigns that leverage multiple assets to achieve their scientific goals. Three distinct categories consisting of seven total unique capabilities related to scientific data sampling and collection were identified and defined. The three broad categories are fundamentally unique, analytically unique, and operationally unique capabilities. This work uses RSC to examine four case studies of DSS missions that achieve new space science goals by leveraging these emergent capabilities. ExoplanetSat leverages shared sampling to conduct observations of necessary frequency and length to detect transiting exoplanets. HOBOCOP leverages simultaneous sampling and stacked sampling to study the Sun in far greater detail than any previous mission. ÆGIR leverages census sampling and self-sampling to catalog asteroids for future ISRU and mining operations. GANGMIR leverages staged sampling with sacrifice sampling and stacked sampling to answer fundamental questions related to the future human exploration of Mars. In all four case studies, RSC showed how scientific value was gained that would. be impossible or unsatisfactory with monolithic systems. Information gained in these studies helped stakeholders more accurately understand the risks and opportunities that arise as a result of the added flexibility in these missions. The wide scope of these case studies demonstrates how RSC can be applied to any science mission, especially one with goals that are more easily achieved with (or impossible to achieve without) DSS. Each study serves as a blueprint for how to conduct a Pre-Phase A study using these methods.