Uncertainty Quantification Using Reduced-Order Models

Date

2019-07-07

Journal Title

Journal ISSN

Volume Title

Publisher

49th International Conference on Environmental Systems

Abstract

Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity thermal models (e.g. Thermal Desktop® models). An approach for creating these surrogates using efficient sampling and Gaussian process data fitting was developed and successfully applied to a broad range of spacecraft applications. This approach provides numerous benefits including computational speed; however, uncertainty must be carefully evaluated.

This investigation attempts to understand, quantify, and mitigate uncertainty using ROMs. An overview of sources of uncertainty will be presented. Further, computational efficiency and uncertainty will be evaluated as a function of surrogate construction. Finally, an overview of how Uncertainty Quantification can be realized using ROMs will be presented. Examples of how these methods can be used in practice will be provided.

Description

Derek Hengeveld, LoadPath, USA
Jacob Moulton, LoadPath, USA
ICES207: Thermal and Environmental Control Engineering Analysis and Software
The 49th International Conference on Environmental Systems as held in Boston, Massachusetts, USA on 07 July 2019 through 11 July 2019.

Keywords

surrogate model, reduced-order model, uncertainty quantification, Thermal Desktop, thermal design

Citation