Speakers
Dr
Emilio De Pasquale
(European Space Agency)Mr
Simone Flavio Rafano Carnà
(Politecnico di Milano)
Description
The risk reduction measures required for the reentry of a spacecraft at its end of life are regulated in Europe by requirements documented in Space Agencies’ instructions and guidelines. In particular, according to the French law, “the operator responsible of a spacecraft controlled reentry shall identify and compute the impact zones of the spacecraft and its fragments for all controlled reentry on the Earth with a probability respectively of 99% and 99,999% taking into account the uncertainties associated to the parameters of the reentry trajectories”. According to European Space Agency guidelines the Safety Re-entry Area (SRA) delimits the area where the debris should be enclosed with a probability of 99,999%.
The computation of SRA is required for a significant number of space missions like spacecraft in low Earth orbits at its end of life and last stages of launchers that shall be controlled to a destructive reentry.
This information is crucial to get safety clearance. A similar box, relative to smaller probability level (99%), is required to implement the procedures of warning and alerting the maritime and aeronautic traffic authorities of the concerned countries.
The dynamics of a space object like a spacecraft or a rocket stage entering the atmosphere is quite complicated and quite sensitive to a number of parameters linked to the fragmentation process, to the trajectory models and to the initial conditions of the arc hitting the atmosphere. The computation of the SRA must take into account the uncertainties of those parameters with a satisfactory accuracy or sufficient level of conservatism to ensure that debris will not fall outside the SRA with a probability larger than 0.001%.
There are two main modeling aspects for the computation of the SRA:
1) the characterization of the fragmentation and explosion process and of the properties of surviving fragments
2) the computation of the impact points from the trajectory propagation taking into account dynamics model and initial conditions uncertainties.
The problem is complicated due to the extremely low probability of interest, which makes quite difficult and inaccurate to use classic statistical techniques and requires to rely on specific extrapolation of the results by fitting distributions tails.
A Montecarlo analysis may be performed to estimate the footprint of fragments impacts. The limitation of this method is the number of dispersed fragments impact points to be simulated by the Montecarlo analysis in order to measure the size of the footprint box associated to a low probability (e.g. 0.001%) of occurrence. The number of samples generated as output of the Montecarlo is constrained by computational time and particular statistical tools are used to estimate the quantile of interests when the number of outputs samples are smaller than required.
This paper describes an innovative method to compute the SRA, considering that the input models and its uncertainties are well defined. The method focuses on the statistical distribution of the uncertainties of necessary input parameters contrary to classical methods that generates a large number of impact points with Montecarlo simulation and processes the outputs of this computation.
As a different approach, the innovative method described in this paper processes only sets of the input dispersions associated to a given probability and, consequently, does not require generating Montecarlo simulations and processing the statistics of the output. The probability of interest is computed integrating the multivariate density function of the input parameters and, then, an optimization process is used to find the output worst case among a reduced set of inputs corresponding to a given probability. Three advantages of extreme importance can be recognized: the probability is computed constraining the input dispersions, that are directly associated to the causes driving the phenomena; a large amount of computational time is saved since a Montecarlo simulation is not required; the level of probability can be arbitrarily small because the computational time is not quite sensitive to the probability level to be achieved.
The drawback of the method is related to the simplification that is introduced in the computation of the overall input probability. This simplification leads, in some cases, to an overestimation of the size of the box providing a conservative solution to the problem.
The method is applied to an example of the SRA computation for the destructive shallow re-entry of a large space vehicle in the South Pacific Ocean. When a vehicle performs a shallow re-entry, it travels on a final orbit whose perigee radius is larger than the Earth radius and it impacts the atmosphere with a flight path angle shallower than usual. Consequently, the spacecraft is exposed for a long period to particular aero-thermo-dynamic conditions, which enlarge significantly the dispersion of the ground impact area of the surviving fragments and makes particularly challenging the estimation of the SRA.
The paper presents the results of the computation of the shallow re-entry SRA using the classical Montecarlo approach and this innovative approach. The results are compared highlighting advantages and drawbacks in terms of accuracy, level of conservatism and computational time.
The method described in this paper is suitable for many future applications taking advantage of its computational speed and reliability: the destructive controlled re-entry of large structures, including in particular the International Space Station (ISS) and the ISS visiting vehicles at its End of Life (EoL); the destructive re-entry of large uncooperative satellites orbiting LEO and MEO as conclusive event of the Active Debris Removal (ADR) technology; the destructive controlled re-entry of last stages of launchers.
| Applicant type | First author |
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Primary authors
Dr
Emilio De Pasquale
(European Space Agency)
Mr
Simone Flavio Rafano Carnà
(Politecnico di Milano)
Co-authors
Mr
Laurent Arzel
(PWC Strategy&)
Prof.
Michèle Lavagna
(Politecnico di Milano)
