Decision-Making and Optimization Framework for the Design of Emerging Satellite Constellations

Koerschner, Marc A.; Krishnan, Kavya Navaneetha; Payan, Alexia P.; Mavris, Dimitri N.

Title:

Decision-Making and Optimization Framework for the Design of Emerging Satellite Constellations

dc.contributor.author	Koerschner, Marc A.
dc.contributor.author	Krishnan, Kavya Navaneetha
dc.contributor.author	Payan, Alexia P.
dc.contributor.author	Mavris, Dimitri N.
dc.contributor.corporatename	Georgia Institute of Technology. Aerospace Systems Design Laboratory	en_US
dc.contributor.corporatename	American Institute of Aeronautics and Astronautics
dc.contributor.corporatename	Georgia Institute of Technology. Aerospace Systems Design Laboratory
dc.date.accessioned	2023-02-06T14:11:00Z
dc.date.available	2023-02-06T14:11:00Z
dc.date.issued	2023-01
dc.description	Presented at AIAA SciTech 2023, National Harbor, MD January 23rd -27th, 2023	en_US
dc.description.abstract	With the parallel increase in global orbital debris due to passive object collisions, as well as in the number of proposed low earth orbit mega-constellations, in anti-satellite missile tests, and the fielding of new satellites, there is an inherent need for a framework to optimize the design of Low Earth Orbit (LEO) mega-constellations to avoid collisions while maintaining the functionality of the constellation. In this paper, we aim to provide a framework that unifies these considerations in the conceptual design phase of mega-constellations. We start with a discussion of metrics of importance for the design of mega-constellations, namely coverage, collision risk, collision avoidance, and station-keeping costs. With these metrics defined, we utilize the first principles of orbital mechanics and statistical models to analyze potential alternative mega-constellation designs. These designs are then optimized using Non-denominated Sorting Genetic Algorithm 2 (NSGA2) with our own defined objective function to create a repository of Pareto optimal configurations. We then showcase how a multi-criteria decision-making methodology can be utilized by a variety of unique stakeholders and subject-matter experts to select an optimal constellation design for a given scenario. A Pareto Frontier collection with optimal solutions of 10 constellations was produced by the framework. Radar plots to assess the significance of the weighted metric of the framework shows several trading options for conceptual designs of the constellations. We finally discuss the scope, limitations, applications, and future work for various scenarios.	en_US
dc.identifier.citation	Marc A. Koerschner, Kavya Navaneetha Krishnan, Alexia P. Payan and Dimitri N. Mavris. "Decision-Making and Optimization Framework for the Design of Emerging Satellite Constellations," AIAA 2023-2549. AIAA SCITECH 2023 Forum. January 2023. DOI: https://doi.org/10.2514/6.2023-2549	en_US
dc.identifier.doi	https://doi.org/10.2514/6.2023-2549	en_US
dc.identifier.uri	http://hdl.handle.net/1853/70264
dc.language.iso	en_US	en_US
dc.publisher	AIAA	en_US
dc.publisher	Georgia Institute of Technology
dc.publisher.original	American Institute of Aeronautics and Astronautics (AIAA)
dc.relation.ispartofseries	ASDL; AIAA
dc.title	Decision-Making and Optimization Framework for the Design of Emerging Satellite Constellations	en_US
dc.type	Text
dc.type.genre	Paper
dspace.entity.type	Publication
local.contributor.author	Payan, Alexia P.
local.contributor.author	Mavris, Dimitri N.
local.contributor.corporatename	Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename	Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename	College of Engineering
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