Assessing Science Robustness in Uncertain Environments: Application to a Uranus Flagship Mission

Spacecraft missions to previously unexplored bodies can face high uncertainty when defining their science investigations. This uncertainty presents a challenge for mission design and instrument concept of operations, which need to be optimized to meet the mission science objectives. Mission teams increasingly rely on uncertainty quantification to assess and mitigate the risks to space systems or mission-critical events. However, an end-to-end framework has yet to be developed that propagates uncertainty from science models to the measurements and mission design requirements to assist in designing robust concepts of operations. The proposed methodology develops a science systems engineering framework, integrating a science model with trajectory designs to compute quantitative science value metrics. The science model is established by identifying relevant physical models (governing equations and modeling assumptions) and input variables from the literature, simulation data, and past missions. Variables are defined with probability distributions and Monte Carlo simulations are run to quantify the uncertainties. For a given trajectory, the model outputs predictive probability distributions of the science value metrics, highlighting the trajectory’s science performance and its robustness to uncertainty in the physical process. The framework is applicable to any mission targeting highly dynamic and uncertain processes. This paper demonstrates its application to a future Uranus Flagship mission, focusing on magnetosphere science objectives. Listed as the highest priority Flagship mission by the latest Decadal Survey, a mission to the Uranian system aims to answer science questions regarding Uranus’s interior and atmosphere, its satellites and rings, and its magnetosphere. Analytic and numerical models have been developed to understand Uranus’ magnetosphere; however, significant uncertainties remain, leading to challenges when defining magnetosphere science investigations. By applying the proposed methodology, this paper shows a significant variation in predicted science metrics of interest (e.g., number of magnetopause crossings) that can be expected from similar trajectories due to varying environment conditions (solar wind and interplanetary magnetic field) or different arrival times at Uranus. These results should inform the flow-down of measurement requirements to mission design requirements for magnetosphere science.