CubeSat mission design software tool for risk estimating relationships

In an effort to make the CubeSat risk estimation and management process more scientific, a software tool has been created that enables mission designers to estimate mission risks. CubeSat mission designers are able to input mission characteristics, such as form factor, mass, development cycle, and launch information, in order to determine the mission risk root causes which historically present the highest risk for their mission. Historical data was collected from the CubeSat community and analyzed to provide a statistical background to characterize these Risk Estimating Relationships (RERs). This paper develops and validates the mathematical model based on the same cost estimating relationship methodology used by the Unmanned Spacecraft Cost Model (USCM) and the Small Satellite Cost Model (SSCM). The RER development uses general error regression models to determine the best fit relationship between root cause consequence and likelihood values and the input factors of interest. These root causes are combined into seven overall CubeSat mission risks which are then graphed on the industry-standard 5×5 Likelihood-Consequence (L-C) chart to help mission designers quickly identify areas of concern within their mission. This paper is the first to document not only the creation of a historical database of CubeSat mission risks, but, more importantly, the scientific representation of Risk Estimating Relationships.