Lane, Kayton Anne2024-01-052024-01-052024-01-04vt_gsexam:39023https://hdl.handle.net/10919/117305Earth's atmosphere is a dynamic region with a complex interplay of energetic inputs, outputs, and transport mechanisms. A complete understanding of the atmosphere and how various fields within it interact is essential for predicting atmospheric shifts relevant for spaceflight, the evolution of Earth's climate, radio communications, and other practical applications. In-situ observations of a critical altitude region within Earth's atmosphere from 100-200 km in altitude, a subset of a larger 90 – 400 km altitude region deemed the "Thermospheric Gap", are required for constraining atmospheric models of wind, temperature, and density perturbations caused by atmospheric tides. Observations within this region that are sufficient to fully reconstruct and understand the evolution of tides therein are nonexistent. Certain missions have sought to fill portions of this observation gap, including Daedalus which was selected as a candidate for the Earth Explorer program by the European Space Agency in 2018. This study focuses on the design and optimization of a two-satellite, highly elliptical satellite constellation to perform in-situ observations and reconstruction of tidal features in the 100-200 km region. The model atmosphere for retrieving sample data is composed of DE3 and DE2 tidal features from the Climatological Model of the Thermosphere (CTMT) and background winds from the Thermosphere-Ionosphere-Electrodynamic General Circulation Model (TIEGCM). BoTorch, a Bayesian Optimization package for Python, is integrated with the Ansys Systems Tool Kit (STK) to model the constellation's propagation and simulated atmospheric sampling. A least squares fitting algorithm is utilized to fit the sampled data to a known tidal function form. Key results include 14 Pareto optimal solutions for the satellite constellation based on a set of 7 objective functions, 3 constellation input parameters, and a sample set of n = 86. Four of these solutions are discussed in more detail. The first two are the best and second-best options on the Pareto front for sampling and reconstruction of the input tidal fields. The third is the best solution for latitudinal tidal fitting coverage. The fourth is a compromise solution that nearly minimizes delta-v expenditure, while sacrificing some quality in tidal fitting and fitting coverage.ETDenIn CopyrightThermospheric GapBayesian OptimizationSatellite ConstellationAtmospheric TidesLower Thermosphere IonosphereThesis