A methodology to account for dynamic variability in micro-vibration analysis of satellites

Nominal operability of satellites can be significantly affected by low level vibrations in the range of micro-g generated by on board mechanisms. These are referred to as micro-vibrations and can considerably jeopardise the regular functioning of very high precision sensors. Hence, it is essential to study their features in terms of characterisation and analysis, which are greatly affected by structural uncertainties due, among other causes, to the manufacturing and assembly tolerances. In this thesis the variability of micro-vibrations arising from structural uncertainties is targeted, through the implementation of a macroscopic approach that is able to take into account different sources of structural uncertainties. In particular the work done in this doctorate is split into two main areas. First the characterisation of micro-vibration sources is studied; these include all the devices on board the spacecraft that can generate micro-vibrations. A methodology to investigate the effects of manufacturing defects on the dynamics of reaction wheels is described, by focusing on the frequency domain representation of their disturbance. Through this approach, it is possible to define a group of nominally-identical devices by means of a single disturbance input matrix. Such achievement can be beneficial in space applications as it allows an easier assessment of micro-vibrations on the spacecraft. Second, the issue of structural uncertainty is addressed in terms of transmission path from the source to the receiver. The focus here is shifted towards the development of an analysis methodology that can target the issue of structural uncertainties by satisfying the stringent computational requirements for aerospace applications. In particular, the main achievement obtained in this thesis stays in the development of an uncertainty quantification methodology that can be used to provide an estimation of perturbation parameters used in the Craig-Bampton Stochastic Method