Drag reduction through shape optimisation for satellites in Very Low Earth Orbit. (February 2021)

• Record Type: Journal Article
• Authors: Walsh, Jonathan; Berthoud, Lucy; Allen, Christian
• Is Part Of: Acta astronautica. Volume 179(2021)
• Page Start: 105
• Page End: 121
• Publication Date: 2021-02
• Subjects: 00-01 -- 99-00; VLEO -- Shape optimisation -- Surrogate modelling -- DSMC; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2020.09.018
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: Operating satellites at altitudes in Very Low Earth Orbit (VLEO) has many advantages. However, due to the higher atmospheric density of this region, satellites encounter significantly higher atmospheric drag. Depending on the mission, this may require a propulsive system to maintain the orbit which costs both fuel mass and volume. It is therefore desirable to reduce the drag in order to either reduce these costs or to extend the operational life. In this paper a series of viable aeroshell profiles are identified for satellites operating in VLEO using a Radial Basis Function-based surrogate model with data generated using both Panel Methods and Discrete Simulation Monte Carlo simulations. It was demonstrated that a maximum drag reduction of between 21% and 35% was achievable for the profiles when optimising a bi-conic profile for minimum drag based on Discreet Simulation Monte Carlo simulations with an energy accommodation coefficient of 0.95. Accounting for the loss of internal volume and assuming the reduction in fuel mass results in an equally proportioned reduction in fuel system volume it was observed that only a 13% to 27% reduction was achieved. Highlights: Bi-conic Aeroshell profiles can be used to reduces aerodynamic drag in VLEO. A reduction in the aerodynamic drag can be achieved by adjusting the tail profile. For equal reduction in volume and drag, blunter Aeroshell profiles are preferable. An application of a Radial Basis Function-based surrogate model trained using DSMC.