Abstract

The development of a novel launch vehicle is a long and costly process. The complexity of incorporating flexible-body dynamics in early design leads to frequent use of a rigid-body assumption until design decisions fix sectional mass properties. This assumption is also necessary to reduce dimensionality and complexity of modeling for benchmarking excitation frequencies and deflections. However, there are significant and difficult to quantify implications of rigid-body simplifications and mass property generalizations. Inadequate understanding of dynamic variability has been traced to costly vehicle redesigns and operating environment changes due in part to the coupling with guidance and navigation. One way to integrate flexible-body dynamics earlier is through reduced order modeling (ROM) methods, by representing the vehicle as a simpler system. However, without proper uncertainty characterization between standard rigid and flexible dynamic analysis techniques ROM can exacerbate these due to sparsity of mass property data. Commonly, performance variability is assessed using a combination of low-fidelity modeling, regression analysis, and probabilistic theory. This research aims to use this approach to identify variability and quantify uncertainty of rigid and flexible-body simplifications. Such a method enables vehicle designers to better understand the modeling implications introduced by varying the mass property distribution in flexible-body dynamics during early phases of design. To characterize the quality of performance outputs, this method compares the variation in dynamic performance prediction from rigid and reduced flexible body representations to a baseline full-scale flexible-body analysis through alternatives of lumped mass properties. Using a launch vehicle design based on publicly available data as a baseline, this research applies regression and probability theory to predict and characterize variability of dynamic models to inform a threshold for which the mass distribution can be generalized before performance variation is no longer acceptable. Model quality is ensured by this analysis through predictive error quantification as well as sensitivity to mass distribution. This method provides a quantitative uncertainty basis for decision making when adequate mass property calibration data is not accessible to anchor dynamic models. Development of design margins can thus be supported for highly variable systems with prediction confidence for prospective models.