Active Subspaces Applied to Range Safety Analysis and Optimization

摘要

The commercial space transportation sector is rapidly evolving and growing. The expected increase in activity is reflected in the new types of vehicles being considered and the creation of new spaceports in different parts of the United States. This increases the importance of understanding the uncertainties and applicability of the current methodologies to assess the safety of the uninvolved public in case of a launch or reentry vehicle malfunction. The Federal Aviation Administration (FAA) has a set of licensing analysis and procedures geared towards reducing the risks that commercial space vehicle could pose to the safety of the uninvolved public in case of a malfunction. These analysis and procedures are derived from the space shuttle era, and the possibility exists that they may be overly conservative. In practice, this type of analysis handles a large number of uncertain inputs. To quantify the risks, we are using a tool called Range Safety Assessment Tool (RSAT). In this paper we study the uncertainty effects on the safety metrics and perform optimizations that identify input parameter combinations that can lead to worst case scenarios. We have reformulated the safety assessment process as an optimization problem in which the goal is to decrease the risks to the uninvolved public. The computational cost of performing the optimization and uncertainty studies is high, which is why we are also exploring the use of an Active Subspace Method (ASM) couple with Gaussian Process Regression (GPR) to create a surrogate model which could replace the physical model.