The Southern Ocean has complex spatial variability, characterized by sharp fronts, steeply tilted isopycnals, and deep seasonal mixed layers. Methods of defining Southern Ocean spatial structures traditionally rely on somewhat ad-hoc combinations of physical, chemical, and dynamic properties. As a step towards an alternative approach for describing spatial variability in temperature, here we apply an unsupervised classification technique (that is, Gaussian mixture modelling or GMM) to Southern Ocean Argo float temperature profiles. GMM, without using any latitude or longitude information, automatically identifies several spatially coherent circumpolar classes influenced by the Antarctic Circumpolar Current. In addition, GMM identifies classes that bear the imprint of mode/intermediate water formation and export, large-scale gyre circulation, and the Agulhas Current, among others. Because GMM is robust, standardized, and automated, it can potentially be used to identify structures (such as fronts) in both observational and model datasets, possibly making it a useful complement to existing classification techniques.

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Low-thrust many-revolution trajectory design and orbit transfers are becoming increasingly important with the development of high specific impulse, low-thrust engines. Closed-loop feedback-driven (CLFD) control laws can be used to solve these trajectory design problems with minimal computational cost and offer potential for autonomous guidance. However, they have user-defined parameters which limit their optimality. In this work, an actor-critic reinforcement learning framework is proposed to make the parameters of the Lyapunov-based Q-law state-dependent, ensuring the controller can adapt as the dynamics evolve during a transfer. The proposed framework should be independent of the particular CLFD control law and provides improved solutions for mission analysis. There is also potential for future on-board autonomous use, as trajectories are closed-form and can be generated without an initial guess. The current results focus on GTO-GEO transfers in Keplerian dynamics and later with eclipse and J 2 effects. Both time-optimal and mass-optimal transfers are presented, and the stability to uncertainties in orbit determination are discussed. The task of handling orbit perturbations is left to future work.

Space debris have become exceedingly dangerous over the years as the number of objects in orbit continues to increase. Active debris removal (ADR) missions have gained significant interest as effective means of mitigating the risk of collision between objects in space. This study focuses on developing a multi-ADR mission that utilizes controlled reentry and deorbiting. The mission comprises two spacecraft: a Servicer that brings debris to a low altitude and a Shepherd that rendezvous with the debris to later perform a controlled reentry. A preliminary mission design tool (PMDT) was developed to obtain time and fuel optimal trajectories for the proposed mission while considering the effect of J2, drag, eclipses, and duty cycle. The PMDT can perform such trajectory optimizations for multi-debris missions with computational time under a minute. Three guidance schemes are also studied, taking the PMDT solution as a reference to validate the design methodology and provide guidance solutions to this complex mission profile.

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