Data Models for the Probabilistic Design of the Thermal Protection System of a Reusable Launch Vehicle Stage

Aaron Dexter KOCH, Jascha WILKEN, Marko ALDER

DOI Number: XXX-YYY-ZZZ

Conference number: HiSST 2024-00152

Early design phases significantly impact a system’s life-cycle costs, yet they are fraught with large uncertainties. Hence, it is important to incorporate uncertainties in preliminary design activities. However, sampling the uncertain design space instead of analyzing a single system entails a considerable computational cost. It also demands a high degree of automation, with data models being a crucial component. Consolidating all data describing a system in a structured way in a single source helps to streamline processes. This applies to the technical system itself as well as the description of the probabilistic study. The use of the Extensible Markup Language (XML) along with XML Schema Definition, complemented by libraries written in C++ with Python bindings via Boost.Python, has proven to be effective for implementing data models. This paper demonstrates the application of such data models through the sizing of a thermal protection system (TPS) of a reusable launch vehicle stage. The results indicate that a probabilistic design of a TPS can lead to a reduction in the required material thickness compared to a worst-case scenario.

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Data Model, Probabilistic Design, Reusable Space Transportation System, Uncertainty Quantification

In Categories: 1.Events, HiSST 2024, Thermal and Energy Management

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