Work

Uncertainty quantification data model for the probabilistic design of the thermal protection system of a reusable launch vehicle stage

Year: 2025

Type: article

Abstract: 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. Standardized data models can significantly aid this effort by offering a structured way of storing and handling large amounts of data. Therefore, this paper proposes an uncertainty quantification (UQ) data model for dealing with probabilistic data. Its hierarchical structure has been specified using Extensible Markup Language Schema Definition (XSD). It is complemented by a library written in C and provides additional Python bindings. The proposed approach is used in a case study in which the design of a thermal protection system (TPS) of the wing profile of a reusable launch vehicle stage is assessed. The UQ data model was used to store the UQ data of the TPS design study.

Source: CEAS Space Journal

DOI: 10.1007/s12567-025-00597-9

Authors: Aaron D. Koch, Jascha Wilken, Marko Alder

Institution: Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funder: Deutsches Zentrum für Luft- und Raumfahrt