Uncertainty-Dispersion Analysis of SRM Performance Prediction & Reconstruction

This paper is aimed at presenting fast and effective SRM performance analysis tools and models based on Monte-Carlo method, accounting for uncertainty and dispersion of the SRM ballistics parameters involved in the prediction of the SRM ballistics and the reconstruction of it, following the post-firing analysis of a static firing test (SFT) or a flight. SRM internal ballistics models are used in order to perform both prediction of given SRM configurations (e.g. to evaluate SRM configurations in the frame of trade-off studies, or to perform prediction of the SRM behaviour before firing) and reconstruction/analysis of given SRM configurations, in order to extract from firing data, non-ideal ballistic parameters of the SRM (e.g. hump, scale factor and motor efficiencies) and nozzle throat erosion law. The accounting for uncertainty and dispersion of the input parameters, in both these phases (prediction and reconstruction) is particularly important to ballisticians in order to assess at the given phase, the range of the performance of the SRM expected or the non-ideal parameters range, and their evolution along the motor development (i.e. development or production). This is also affordable by a computational point of view, since both the SRM internal ballistic models and the Monte- Carlo methods are simple and consolidated tools that allow to provide additional information on the SRM along its whole lifecycle. In this paper, a comprehensive analysis is performed to address the effects on SRM prediction and reconstruction of the uncertain and dispersed input parameters, considering also their different quantification during the development and the production phase. Zefiro 23 SRM, second stage of Vega launch vehicle is considered as study case, discussing in particular the additional information that the methodology is able to provide, comparing in a systematic way its application to the development and the production phases. This kind of approach for the SRM analysis (both for the prediction and reconstruction) provides in particular a major and more robust insight on the SRM performance, internal ballistics and non-ideal parameters, enabling the capability to define the model/input uncertainties with respect to product evolution, possible discrepancies, change of process, technologies and materials, along the SRM lifecycle.