The commercialization of space is reshaping the traditional methods of designing and sustaining a system in the emerging Commercial Low Earth Orbit (LEO) Destinations (CLD) market. To enable a successful program over the life of a product, it is critical to know the non-recurring, recurring, and sustaining engineering efforts required to meet performance needs at the onset of the program. One of the key drivers in all three of these aspects is hardware reliability and predicted life. Collins Aerospace, an RTX business, has worked to develop an analysis method and associated model that can be used to predict component life and the impact of life values on a program. Using this method, operation and hardware information informs component life, sub-assembly life, and system life. The outputs of this model include the aforementioned life data as well as the impacts of that data on key design metrics such as initial and lifetime mass. This paper will discuss the analysis methodology and how the results can be used to inform the initial design of a system, the sustaining engineering plan, and the initial assumptions for Collins' Leto™, an Artificial Intelligence (AI) powered spaceflight intelligence system. To demonstrate the value of the approach, a case study is described on the Water Processor Assembly (WPA).
Schuster et al. (Sun,) studied this question.