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<p>This study proposes a novel analytical method for rapid and conservative estimation of debris dispersion areas following the generation of debris from a launch vehicle. The method introduces the F&amp;G approach for dispersion prediction, incorporating projection techniques and singular value decomposition (SVD) to analytically characterize the spread of debris. Unlike conventional statistical approaches, such as Monte Carlo Simulation (MC) and Unscented Transform (UT), the proposed method does not require explicit generation of debris fragments. Its performance was quantitatively evaluated through comparisons with MC and UT, demonstrating comparable accuracy with an average error within 0.15%. Furthermore, this method maintains a nearly constant computation time and exhibits a significantly faster performance than conventional methods, particularly as the number of potential fragments increases. Its effectiveness was validated using real flight data from the second launch of KSLV-II, demonstrating its strong potential as a lightweight and reliable algorithm for real-time onboard debris dispersion prediction.</p>

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Estimation of Launch Vehicle Debris Dispersion Area Using the F&G SVD Method

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Bum-Yong Park,
  • Dong-Hyun Cho,
  • Tae-Hun Kim,
  • Ha-Ryong Song

摘要

This study proposes a novel analytical method for rapid and conservative estimation of debris dispersion areas following the generation of debris from a launch vehicle. The method introduces the F&G approach for dispersion prediction, incorporating projection techniques and singular value decomposition (SVD) to analytically characterize the spread of debris. Unlike conventional statistical approaches, such as Monte Carlo Simulation (MC) and Unscented Transform (UT), the proposed method does not require explicit generation of debris fragments. Its performance was quantitatively evaluated through comparisons with MC and UT, demonstrating comparable accuracy with an average error within 0.15%. Furthermore, this method maintains a nearly constant computation time and exhibits a significantly faster performance than conventional methods, particularly as the number of potential fragments increases. Its effectiveness was validated using real flight data from the second launch of KSLV-II, demonstrating its strong potential as a lightweight and reliable algorithm for real-time onboard debris dispersion prediction.