Policy and Design Courses of Action to Improve Resilience of Proliferated Low Earth Orbit Constellations Against Adverse Solar Weather

There are three main questions answered by this thesis: 1) Would an extreme event on a scale commensurate to historically observed events induce catastrophic failures to current New Space mega-constellations? 2) How do increasing levels of constellation proliferation alter resilience to adverse solar weather? And 3) How do increasing levels of constellation proliferation alter the effectiveness of courses of action to improve resilience? In order to answer these questions, solar weather effects are modeled using a unique process of correlating solar weather event intensities to radiation effects leading to failure. Representative constellation populations are dynamically altered with respect to a Monte Carlo based stochastic simulation of solar cycle 25. The performance degradation, value, and resilience of each system is recorded throughout a solar cycle in baseline cases and then compared to cases employing alternative designs or policy criteria. The results of this thesis show that New Space architectures are resilient to the radiation effects of even extreme case scenarios of solar weather. The results also show that, of the parameters tested, shielding is among the most effective for improving the resilience of highly proliferated systems. The results also imply that increasing manufacturing and launch timelines are most effective at improving resilience when increased together, rather than one parameter in isolation. Finally, through a net present value analysis, this thesis demonstrates how policies may be valued and assessed. A sample valuation for an emergency launch insurance policy is shown in the results as well as evidence supporting “Careful COTS” as a viable and effective methodology for ensuring resilience of COTS-enabled, proliferated systems. All code, datasets, and sample results depicted are provided in a GitHub at the end of the thesis.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Novak, Jonathan

Advisor dc:contributor.advisor

• Hastings, Daniel E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144977

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144977