Speaker
Description
The long-term evolution of the space object population is increasingly difficult to characterize due to rapid changes in launch activity, spacecraft design, and operational behavior in Low Earth Orbit (LEO). Models developed under relatively stable conditions are now applied to a dynamic environment shaped by large constellations, high deployment rates, and diverse operator practices. These changes introduce significant variability in projections of debris growth, reducing confidence in long-horizon forecasts that support both technical assessment and regulatory decision-making.
This study examines these challenges using the Enhanced Monte Carlo Orbital Capacity Assessment Tool (EMOCAT), developed at Embry-Riddle Aeronautical University as an extension of the Massachusetts Institute of Technology’s Orbital Capacity Assessment Tool (MOCAT). EMOCAT evolves object populations through Monte Carlo sampling of launch schedules, fragmentation events, and maneuver uncertainties to estimate collision rates, population growth, and regime stability. While this structure enables probabilistic analysis, outcomes depend strongly on assumptions about future system inputs.
One key source of variability lies in traffic projections, including constellation scaling, orbital shell allocation, and mission-specific deployment strategies. Small changes in these inputs lead to substantially different population trajectories, complicating efforts to identify stable operating conditions. At the same time, evolving technologies such as propulsion reliability, autonomous avoidance systems, and disposal performance introduce additional uncertainty that is not easily captured within fixed parameter sets.
Another challenge arises from the selection of orbital propagators. Current development within EMOCAT incorporates both simplified analytical approaches and higher-fidelity numerical methods. Differences in perturbation modeling, resolution, and computational constraints lead to divergence in long-term orbital states, particularly over extended time horizons. These variations affect predicted conjunction rates and debris distribution, highlighting trade-offs between scalability and physical accuracy in large simulation ensembles.
Limitations also persist in representing fragmentation behavior and operational response, including incomplete breakup characterization and variability in maneuver execution. To address these issues, EMOCAT supports ensemble-based scenario analysis, sensitivity testing across input assumptions, and comparison of propagation approaches. A graphical user interface provides visualization of population evolution, collision likelihood, and uncertainty bounds, enabling interpretation of divergent outcomes across scenarios.
These technical limitations directly influence policy development. The companion policy study shows that capacity-based mitigation strategies rely on credible projections of future conditions. Uncertainty in traffic assumptions, technology performance, and propagation methods introduces ambiguity into policy evaluation and reduces confidence in regulatory thresholds. This work highlights the need for adaptive modeling approaches that explicitly account for uncertainty, enabling more robust integration of simulation outputs into capacity-driven governance of the orbital environment.
| Which section would you like to submit your abstract to? | Session 2: “Challenges of space debris modelling” |
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