An Analysis Tool for Flight Dynamics Monte Carlo Simulations



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Spacecraft design is inherently difficult due to the nonlinearity of the systems involved as well as the expense of testing hardware in a realistic environment. The number and cost of flight tests can be reduced by performing extensive simulation and analysis work to understand vehicle operating limits and identify circumstances that lead to mission failure. A Monte Carlo simulation approach that varies a wide range of physical parameters is typically used to generate thousands of test cases. Currently, the data analysis process for a fully integrated spacecraft is mostly performed manually on a case-by-case basis, often requiring several analysts to write additional scripts in order to sort through the large data sets. There is no single method that can be used to identify these complex variable interactions in a reliable and timely manner as well as be applied to a wide range of flight dynamics problems.

This dissertation investigates the feasibility of a unified, general approach to the process of analyzing flight dynamics Monte Carlo data. The main contribution of this work is the development of a systematic approach to finding and ranking the most influential variables and combinations of variables for a given system failure. Specifically, a practical and interactive analysis tool that uses tractable pattern recognition methods to automate the analysis process has been developed. The analysis tool has two main parts: the analysis of individual influential variables and the analysis of influential combinations of variables. This dissertation describes in detail the two main algorithms used: kernel density estimation and nearest neighbors. Both are non-parametric density estimation methods that are used to analyze hundreds of variables and combinations thereof to provide an analyst with insightful information about the potential cause for a specific system failure. Examples of dynamical systems analysis tasks using the tool are provided.