A rocket debris impact threat assessment method incorporating value distribution of ground avoidance zones

Abstract

With increasing launch frequency in China, rocket debris impact threats have become significant.Traditional trajectory methods often neglect nonlinear stochastic factors and spatial heterogeneity of population activities, leading to risk assessment deviations.This study aims to develop a comprehensive threat assessment framework to enhance statistical confidence in impact prediction.Employing Monte Carlo simulations incorporating mass deviations, engine parameters, and stochastic wind fields, we modeled first-stage debris impact points.A bivariate normal distribution characterization constrained by a 99%confidence ellipse is established to describe impact point dispersion.The proposed data-driven framework integrates stochastic trajectory simulation and fine-grained population data, providing a quantitative approach for ground risk evaluation.Utilizing high-resolution WorldPop data in China and a"severity"index, we optimized ground avoidance zone polygons via boundary merging algorithms.Results demonstrate that impact points exhibit a distinct two-dimensional normal distribution under random couplings.Risk assessment reveals that cumulative risk values increase significantly when intersecting high-density areas.The proposed data-driven framework effectively integrates Monte Carlo simulation with multi-source stochastic disturbances and population data, providing a quantitative method for risk evaluation.The comprehensive risk value(R)offers a single, actionable metric to optimize launch operations and mitigate casualties.