Predicting the vulnerability of spacecraft components: Modelling debris impact effects through vulnerable-zones

Abstract

The space environment around the Earth is populated by more than 130 million objects of 1 mm in size and larger, and future predictions shows that this amount is destined to increase, even if mitigation measures are implemented at a far better rate than today. These objects can hit and damage a spacecraft or its components. It is thus necessary to assess the risk level for a satellite during its mission lifetime. Few software packages perform this analysis, and most of them employ time-consuming ray-tracing methodology, where particles are randomly sampled from relevant distributions. In addition, they tend not to consider the risk associated with the secondary debris clouds. The paper presents the development of a vulnerability assessment model, which relies on a fully statistical procedure: the debris fluxes are directly used combining them with the concept of vulnerable zone, avoiding the random sampling the debris fluxes. A novel methodology is presented to predict damage on internal components. It models the interaction between the components and the secondary debris cloud through basic geometrical operations, considering mutual shielding and shadowing between internal components. The methodologies are tested against state-of-the-art software for relevant test cases, comparing results on external structures and internal components.

Constrained optimisation of preliminary spacecraft configurations under the design-for-demise paradigm

“…To model this phenomenon, the Schafer-Ryan-Lambert (SRL) BLE [9,28] has been used, which takes into account impacts on multi-walled structures. The procedure is also based on the concept of vulnerable zones [8,11,12]. They consist of an adjusted projection of an inner component onto the outer spacecraft structure.…”

“…In fact, the effect of the resulting internal propagation of the ejecta can be significantly influenced by the presence of interposing components between the impacted panel and the target component. A more comprehensive description of the survivability model and its features can be found in [10,11,12,20,21,22].…”

“…For this purpose, we perform the constrained optimisation on the same test case presented in [11], where the constraints where not implemented yet in the framework. Despite the mission scenario and spacecraft configuration are the same as in [11], the optimisation simulations have been re-run also for the unconstrained case, because new features have been introduced in the survivability model [12]. In the following, we give an overview of the characteristics of the test case; for a more detailed description the reader can refer to [11].…”

“…Table A. 12 shows an extract of the materials available in the code, for both the demisability and the survivability models. The majority of the data is from the Debris Assessment Software 2.0 [14,40] database.…”

“…As the problem is non-linear and involves the combination of continuous and discrete variables, classical derivative based approaches are unsuited and a genetic algorithm was selected instead. Moreover, the framework relies on two models [11,12] to assess the demisability and the survivability as a function of several design parameters, and on two indices, which are used to evaluate the level of demisability and survivability and serve as fitness functions in the optimisation process.…”