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

Abstract

Defining science objectives for missions to unexplored bodies can be difficult when the underlying processes and mechanisms are not well understood. This uncertainty presents a challenge when attempting to determine mission requirements to address these objectives. Additionally, uncertainties in the environment may present risks to the system and mission operations. To this end, uncertainty quantification is increasingly used to inform and validate mission design. However, a framework has yet to be developed to support trajectory tradespace exploration of missions targeting uncertain environments through science modeling. The proposed methodology develops a science systems engineering framework integrating a science representation with trajectory designs to compute quantitative science value metrics. The science model is established by identifying relevant physical models (such as governing equations and assumptions) and input variables from the literature, simulation data, as well as past mission results. Variables are defined with probability distributions, and Monte Carlo simulations are used to quantify the uncertainties. For a given trajectory, the analysis outputs predictive probability distributions of the science value metrics, highlighting the trajectory's science performance and its robustness to uncertainty in the physical processes. 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.