JOURNAL ARTICLE

Reliability analysis of space debris mitigation strategies using the Monte Carlo method.

  • Published In: Mathematics in Engineering, Science & Aerospace (MESA), 2025, v. 16, n. 2. P. 319 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Alves de Lima, Matheus; Araújo da Silva, Marcelo 3 of 3

Abstract

In light of growing space exploration, the risk of collisions involving satellites, rockets, and the International Space Station has increased significantly due to the growing number of space debris (objects in orbit that are no longer useful). In this scenario, several public and private organizations have developed strategies to mitigate this problem. The CBERS-1 satellite, launched in 1999 in a Brazil-China collaboration, is still in orbit despite being decommissioned in 2003. This study aims to evaluate the effectiveness and reliability of various mitigation strategies that could have been implemented during the decommissioning of CBERS-1, using the DRAMA (Debris Risk Assessment and Mitigation Analysis) program and the OSCAR (Orbital Spacecraft Active Removal) application, which uses the Monte Carlo method. The objective is to ensure that CBERS-1 re-enters the Earth6 atmosphere within a period of 25 years, meeting the ESA (European Space Agency) space debris mitigation requirements. This analysis contributes to understanding and improving space debris mitigation practices in the context of increasing activity in space. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Mathematics in Engineering, Science & Aerospace (MESA). 2025/06, Vol. 16, Issue 2, p319
  • Document Type:Article
  • Subject Area:Astronomy and Astrophysics
  • Publication Date:2025
  • ISSN:2041-3165
  • Accession Number:186433629
  • Copyright Statement:Copyright of Mathematics in Engineering, Science & Aerospace (MESA) is the property of Nonlinear Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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