Optimising Satellite Geometries to Minimise Drag in Very Low Earth Orbits

Abstract

Very low Earth orbit (VLEO) satellite flight, at less than around 450 km altitude, is becoming increasingly popular. It affords numerous benefits such as increased payload resolution and assured end-of-life de-orbiting due to the presence of atmospheric drag. However, if unaccounted for in mission design, drag will also prematurely de-orbit the satellite. Thus, it is advantageous to reduce the drag force on a satellite as much as possible. One of the factors significantly affecting drag is the outer spacecraft geometry. While previous attempts at drag-oriented design have been made, they considerably sim- plify the case either by looking at a reduced set of parameters or assuming axisymmetry in reducing a 3D case to a 2D representation. The aim of this thesis is to use computational optimisation in 3D to improve the current understanding of the influence of satellite body geometry on the quality of flight in VLEO. This is primarily achieved through drag reduction, but also through the analysis of other realistic mission requirements such as payload carrying ability and controllability. Firstly, recent satellite launches and important missions to VLEO are examined. A clear year-on-year increase in satellite flight to this orbital range is seen, notwithstanding the influence of the Coronavirus pandemic which delayed most launches. This increase is analysed in the context of the current industry trends toward standardisation and miniaturisation. While these trends have some observable benefits, most recent missions to VLEO have been individually engineered in order to maximise their science potential. Thus, bespoke engineering is identified to be appropriate for the objective at hand. An overview of the literature regarding VLEO is presented, both current and historical, and a summary of the atmospheric models which can recreate the conditions therein. Computational drag analysis in VLEO is itself challenging. A panel method program with shadowing analysis called ADBSat, developed and refined in previous studies at Manchester, is employed to this end. The methodology and physical models used therein are comprehensively detailed. A thorough validation is presented, proving good accuracy for its intended purpose. While not universally accurate, particularly for concave geometries, it is fast enough to remove the need for interpolation of drag data, thereby reducing the number of assumptions and simplifications necessary. Another advantage is its implementation in MATLAB, which offers a number of beneficial options for performing optimisation. The limitations of ADBSat can be mitigated through appropriate constraints on the optimisation framework. Having characterised ADBSat, it is integrated into an optimisation framework which uses the genetic algorithm (GA) metaheuristic to improve on satellite body designs. A thorough overview of the many choices for computational optimisation that exist is provided. The specific characteristics of the GA which make it particularly suitable for this problem are identified: its well-tested implementation in MATLAB, its ability to handle problems with a high number of objectives, its suitability for parallelisation, and its population-based approach. The optimisation objectives are drag coefficient multiplied by frontal area, volume, the relative position of the centres of gravity and pressure, and two controllability characteristics. Constraints on the problem include aspect ratio, volume, plate intersection, and shape convexity, with the aim of maintaining feasibility of results both in terms of real-life applications and in terms of analysis by ADBSat. The novel method of employing the GA in conjunction with ADBSat has identified a wide variety of suitable shapes which exhibit trade-offs across the multiple objectives. A general improvement in all five optimisation objectives is seen across the population. Considerable advantages are seen when comparing the multi-objective optimisation to the s

Date of Award	31 Dec 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Nicholas Crisp (Supervisor), Katharine Smith (Supervisor) & Peter Roberts (Supervisor)