Natural Disaster-Resilient Spaceport Network Planning
dc.contributor.author | Wu, Haochen | |
dc.contributor.author | Lin, Yu Syuan | |
dc.contributor.author | Sun, Kevin | |
dc.contributor.author | Koduru, Teja | |
dc.contributor.author | Cinar, Gokcin | |
dc.contributor.author | Johnson, Aaron | |
dc.contributor.author | Jia-Richards, Oliver | |
dc.contributor.author | Li, Max | |
dc.date.accessioned | 2024-07-28T00:48:27Z | |
dc.date.available | 2024-07-28T00:48:27Z | |
dc.date.issued | 2024-07 | |
dc.identifier.citation | AIAA ASCEND, AIAA 2024-4930, Las Vegas, NV, USA, 2024 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/194120 | en |
dc.description.abstract | Geohazards, including landslides, flooding, and erosion, have consistently posed challenges to US infrastructure. As commercial and governmental space transportation become more widespread, the effect of natural disasters on space launch infrastructure also grows more pronounced. For example, hurricanes have significantly disrupted operations at Cape Canaveral Space Force Station and Kennedy Space Center, notably the Space Launch System tests in 2022. To sustain the growth of the space industry and accommodate future launch demands, new spaceports may need to be constructed: This paper develops a spaceport network design model, in order to rigorously identify new spaceport locations that satisfy launch demand while remaining resilient to natural disaster impacts. We begin with a deterministic facility location planning model, then advance to a chance-constrained (CC) version to address the stochastic nature of natural disasters. We base our probability distributions for natural disaster occurrences on annual frequency data, which supports the formulation of our Chance-Constrained Spaceport Facility Location Planning (CC-SPFLP) model. This model also incorporates impact factors---frequency and duration of different natural disaster types, as well as the spatial correlations between adjacent location candidates to optimize spaceport placement. Our experimental results demonstrate that the CC-SPFLP model ensures a probability of satisfying all demands at a level greater than or equal to 1-epsilon, where epsilon represents a predefined confidence level. | en_US |
dc.description.sponsorship | This work was supported by funding from the University of Michigan College of Engineering through the Seeding To Accelerate Research Themes (START) program. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | American Institute of Aeronautics and Astronautics | en_US |
dc.title | Natural Disaster-Resilient Spaceport Network Planning | en_US |
dc.type | Conference Paper | en_US |
dc.subject.hlbsecondlevel | Aerospace Engineering | |
dc.subject.hlbtoplevel | Engineering | |
dc.contributor.affiliationum | Aerospace Engineering, Department of | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/194120/1/AIAA-2024-4930.pdf | |
dc.identifier.doi | 10.2514/6.2024-4930 | |
dc.identifier.doi | https://dx.doi.org/10.7302/23564 | |
dc.identifier.source | AIAA ASCEND | en_US |
dc.description.filedescription | Description of AIAA-2024-4930.pdf : Main article | |
dc.description.depositor | SELF | en_US |
dc.working.doi | 10.7302/23564 | en_US |
dc.owningcollname | Aerospace Engineering, Department of |
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