A State-Dependent Hybrid Integrator: Optimizing Computational Efficiency in High-Eccentricity Orbits

Overview

In orbital simulations, mathematical methods called integrators are used to predict paths of objects in space over long time periods, however, in highly elliptical orbits, integrators require excessive computational resources or fail and violate laws of physics. I engineered a state-dependent hybrid integrator in Python that uses a dynamic logic gate to switch between mathematical frameworks based on the real-time stress of the orbit. This allowed me to prioritize high-precision calculations only when necessary, reducing computing cost and maintaining control of the system's energy. In extreme stress-tests, my hybrid model successfully prevented failure seen in industry-standard methods while using significantly fewer calculations. It reduced force calculations by 38.5% and outperformed both of its components in energy preservation in high eccentricity. My project provides a practical solution for tracking high-eccentricity objects, like asteroids, using satellites with limited on-board hardware that requires high accuracy and low power consumption, maximizing mission-life.

Awards (2)

• Special Award
• Selected for CWSF 2026

Competition history

• CWSF 2026 Aerospace

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