Session
Session VIII: Advanced Technologies—Section 2
Abstract
Given the dynamic environment in which spacecraft exist, a better methodology for performing orbital lifetime analyses over the current practice of point analyses was desired. The approach chosen was to utilize Monte Carlo based predictions, which provides the ability to gauge the probability of meeting mission lifetime goals, as well as identifying driving factors. The Monte Carlo analysis, called Orbital Lifetime Monte Carlo (OLMC), is based on the NASA Langley Research Center long term orbit propagator Orbital Lifetime. OLMC incorporates the ability to model variations in predictions of solar flux levels and timing of associated peaks, the variation in launch vehicle orbit insertion accuracy (altitude, velocity, and flight path angles), spacecraft ballistic coefficients, and launch delays. Desired repeatability, distribution smoothness and code runtime are considered for the purposes of establishing values for code specific parameters and number of Monte Carlo runs. Results demonstrate that solar flux predictions are the primary driver for variations in lifetime; of which, due to their variability, multiple prediction sets should be utilized to fully characterize the lifetime range of a spacecraft.
Presentation Slides
Stochastic Orbital Lifetime Analysis
Given the dynamic environment in which spacecraft exist, a better methodology for performing orbital lifetime analyses over the current practice of point analyses was desired. The approach chosen was to utilize Monte Carlo based predictions, which provides the ability to gauge the probability of meeting mission lifetime goals, as well as identifying driving factors. The Monte Carlo analysis, called Orbital Lifetime Monte Carlo (OLMC), is based on the NASA Langley Research Center long term orbit propagator Orbital Lifetime. OLMC incorporates the ability to model variations in predictions of solar flux levels and timing of associated peaks, the variation in launch vehicle orbit insertion accuracy (altitude, velocity, and flight path angles), spacecraft ballistic coefficients, and launch delays. Desired repeatability, distribution smoothness and code runtime are considered for the purposes of establishing values for code specific parameters and number of Monte Carlo runs. Results demonstrate that solar flux predictions are the primary driver for variations in lifetime; of which, due to their variability, multiple prediction sets should be utilized to fully characterize the lifetime range of a spacecraft.
