146 research outputs found

    A high order method for orbital conjunctions analysis: Monte Carlo collision probability computation

    Get PDF
    Three methods for the computation of the probability of collision between two space objects are presented. These methods are based on the high order Taylor expansion of the time of closest approach (TCA) and distance of closest approach (DCA) of the two orbiting objects with respect to their initial conditions. The identification of close approaches is first addressed using the nominal objects states. When a close approach is identified, the dependence of the TCA and DCA on the uncertainties in the initial states is efficiently computed with differential algebra (DA) techniques. In the first method the collision probability is estimated via fast DA-based Monte Carlo simulation, in which, for each pair of virtual objects, the DCA is obtained via the fast evaluation of its Taylor expansion. The second and the third methods are the DA version of Line Sampling and Subset Simulation algorithms, respectively. These are introduced to further improve the efficiency and accuracy of Monte Carlo collision probability computation, in particular for cases of very low collision probabilities. The performances of the methods are assessed on orbital conjunctions occurring in different orbital regimes and dynamical models. The probabilities obtained and the associated computational times are compared against standard (i.e. not DA-based) version of the algorithms and analytical methods. The dependence of the collision probability on the initial orbital state covariance is investigated as wel

    Propagation of Large Uncertainty Sets in Orbital Dynamics by Automatic Domain Splitting

    Get PDF
    Current approaches to uncertainty propagation in astrodynamics mainly refer to linearized models or Monte Carlo simulations. Naive linear methods fail in nonlinear dynamics, whereas Monte Carlo simulations tend to be computationally intensive. Differential algebra has already proven to be an efficient compromise by replacing thousands of pointwise integrations of Monte Carlo runs with the fast evaluation of the arbitrary order Taylor expansion of the flow of the dynamics. However, the current implementation of the DA-based high-order uncertainty propagator fails when the non-linearities of the dynamics prohibit good convergence of the Taylor expansion in one or more directions. We solve this issue by introducing automatic domain splitting. During propagation, the polynomial expansion of the current state is split into two polynomials whenever its truncation error reaches a predefined threshold. The resulting set of polynomials accurately tracks uncertainties, even in highly nonlinear dynamics. The method is tested on the propagation of (99942) Apophis post-encounter motion

    Rosetta Philae SD2 Drill System and Its Operation on 67p/Churyumov-Gerasimenko

    Get PDF
    Rosetta Lander Philae approached and landed on the surface of comet 67P/Churyumov-Gerasimenko on the 12th of November 2014. Among the specific Subsystems and instruments carried on board, the Drill, Sample and Distribution System (SD2) which was in charge to drill the surface of the comet, take comet’s soil sample(s) and distribute the collected sample to different instruments. Rosetta has been launched in 2004 and, after very complex orbital trajectories and specific commissioning events, met and carried out a rendezvous with the comet; after ten years cruise and three subsequent touch down, Philae eventually landed on the comet surface. On the 14th of November 2014 SD2 was decided to be operated on the comet. This paper provides an overview of the achievements during the operational phase on the comet and will summarize the basic characteristics and peculiarities of SD2 drill system
    corecore